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1.
《Information processing & management》2019,56(3):526-540
Recommender Systems deal with the issue of overloading information by retrieving the most relevant sources in the wide range of web services. They help users by predicting their interests in many domains like e-government, social networks, e-commerce and entertainment. Collaborative Filtering (CF) is the most promising technique used in recommender systems to give suggestions based on liked-mind users’ preferences. Despite the widespread use of CF in providing personalized recommendation, this technique has problems including cold start, data sparsity and gray sheep. Eventually, these problems lead to the deterioration of the efficiency of CF. Most existing recommendation methods have been proposed to overcome the problems of CF. However, they fail to suggest the top-n recommendations based on the sequencing of the users’ priorities. In this research, to overcome the shortcomings of CF and current recommendation methods in ranking preference dataset, we have used a new graph-based structure to model the users’ priorities and capture the association between users and items. Users’ profiles are created based on their past and current interest. This is done because their interest can change with time. Our proposed algorithm keeps the preferred items of active user at the beginning of the recommendation list. This means these items come under top-n recommendations, which results in satisfaction among users. The experimental results demonstrate that our algorithm archives the significant improvement in comparison with CF and other proposed recommendation methods in terms of recall, precision, f-measure and MAP metrics using two benchmark datasets including MovieLens and Superstore. 相似文献
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《Information processing & management》2022,59(2):102861
Integrating useful input information is essential to provide efficient recommendations to users. In this work, we focus on improving items ratings prediction by merging both multiple contexts and multiple criteria based research directions which were addressed separately in most existent literature. Throughout this article, Criteria refer to the items attributes, while Context denotes the circumstances in which the user uses an item. Our goal is to capture more fine grained preferences to improve items recommendation quality using users’ multiple criteria ratings under specific contextual situations. Therefore, we examine the recommenders’ data from the graph theory based perspective by representing three types of entities (users, contextual situations and criteria) as well as their relationships as a tripartite graph. Upon the assumption that contextually similar users tend to have similar interests for similar item criteria, we perform a high-order co-clustering on the tripartite graph for simultaneously partitioning the graph entities representing users in similar contextual situations and their evaluated item criteria. To predict cluster-based multi-criteria ratings, we introduce an improved rating prediction method that considers the dependency between users and their contextual situations, and also takes into account the correlation between criteria in the prediction process. The predicted multi-criteria ratings are finally aggregated into a single representative output corresponding to an overall item rating. To guide our investigation, we create a research hypothesis to provide insights about the tripartite graph partitioning and design clear and justified preliminary experiments including quantitative and qualitative analyzes to validate it. Further thorough experiments on the two available context-aware multi-criteria datasets, TripAdvisor and Educational, demonstrate that our proposal exhibits substantial improvements over alternative recommendations approaches. 相似文献
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[目的/意义]旨在深入研究情境信息对用户偏好的影响,提高情境感知推荐的准确性。[方法/过程]提出了基于梯度提升决策树的情境感知推荐模型,根据梯度提升决策树计算情境属性权重,将其与传统协同过滤算法相融合,生成情境感知推荐结果。[结果/结论]该模型可以识别影响用户偏好的重要情景属性,为用户提供个性化推荐服务。 相似文献
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《Information processing & management》2023,60(5):103464
Existing approaches to learning path recommendation for online learning communities mainly rely on the individual characteristics of users or the historical records of their learning processes, but pay less attention to the semantics of users’ postings and the context. To facilitate the knowledge understanding and personalized learning of users in online learning communities, it is necessary to conduct a fine-grained analysis of user data to capture their dynamical learning characteristics and potential knowledge levels, so as to recommend appropriate learning paths. In this paper, we propose a fine-grained and multi-context-aware learning path recommendation model for online learning communities based on a knowledge graph. First, we design a multidimensional knowledge graph to solve the problem of monotonous and incomplete entity information presentation of the single layer knowledge graph. Second, we use the topic preference features of users’ postings to determine the starting point of learning paths. We then strengthen the distant relationship of knowledge in the global context using the multidimensional knowledge graph when generating and recommending learning paths. Finally, we build a user background similarity matrix to establish user connections in the local context to recommend users with similar knowledge levels and learning preferences and synchronize their subsequent postings. Experiment results show that the proposed model can recommend appropriate learning paths for users, and the recommended similar users and postings are effective. 相似文献
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《Information processing & management》2020,57(2):102142
With the information explosion of news articles, personalized news recommendation has become important for users to quickly find news that they are interested in. Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods which characterize the content of user reading history. Although these methods have achieved good performances, they still suffer from data sparse problem, since most of them fail to extensively exploit high-order structure information (similar users tend to read similar news articles) in news recommendation systems. In this paper, we propose to build a heterogeneous graph to explicitly model the interactions among users, news and latent topics. The incorporated topic information would help indicate a user’s interest and alleviate the sparsity of user-item interactions. Then we take advantage of graph neural networks to learn user and news representations that encode high-order structure information by propagating embeddings over the graph. The learned user embeddings with complete historic user clicks capture the users’ long-term interests. We also consider a user’s short-term interest using the recent reading history with an attention based LSTM model. Experimental results on real-world datasets show that our proposed model significantly outperforms state-of-the-art methods on news recommendation. 相似文献
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《International Journal of Information Management》2016,36(3):397-402
Online video recommender systems help users find videos suitable for their preferences. However, they have difficulty in identifying dynamic user preferences. In this study, we propose a new recommendation procedure using changes of users’ facial expressions captured every moment. Facial expressions portray the users’ actual emotions about videos. We can utilize them to discover dynamic user preferences. Further, because the proposed procedure does not rely on historical rating or purchase records, it properly addresses the new user problem, that is, the difficulty in recommending products to users whose past rating or purchase records are not available. To validate the recommendation procedure, we conducted experiments with footwear commercial videos. Experiment results show that the proposed procedure outperforms benchmark systems including a random recommendation, an average rating approach, and a typical collaborative filtering approach for recommendation to both new and existing users. From the results, we conclude that facial expressions are a viable element in recommendation. 相似文献
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《Information processing & management》2023,60(2):103205
Personalized recommender systems have been extensively studied in human-centered intelligent systems. Existing recommendation techniques have achieved comparable performance in predictive accuracy; however, the trade-off between recommendation accuracy and diversity poses new challenges, as diversification may lead to accuracy loss, whereas it can solve the over-fitting problem and enhance the user experience. In this study, we propose a heuristic optimization-based recommendation model that jointly optimizes accuracy and diversity performance by obtaining a set of optimized solutions. To establish the best accuracy-diversity balance, a novel trajectory-reinforcement-based bacterial colony optimization algorithm was developed. The improved bacterial colony optimization algorithm was comprehensively evaluated by comparing it with eight popular and state-of-the-art algorithms on ten benchmark testing problems with different degrees of complexity. Furthermore, an optimization-based recommendation model was applied to a real-world recommendation dataset. The results demonstrate that the improved bacterial colony optimization algorithm achieves the best overall performance for benchmark problems in terms of convergence and diversity. In the real-world recommendation task, the proposed approach improved the diversity performance by 1.62% to 8.62% while maintaining superior (1.88% to 40.32%) accuracy performance. Additionally, the proposed personalized recommendation model can provide a set of nondominated solutions instead of a single solution to accommodate the ever-changing preferences of users and service providers. Therefore, this work demonstrates the excellence of an optimization-based recommendation approach for solving the accuracy-diversity trade-off. 相似文献
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《Information processing & management》2022,59(4):102946
Text classification is an important research topic in natural language processing (NLP), and Graph Neural Networks (GNNs) have recently been applied in this task. However, in existing graph-based models, text graphs constructed by rules are not real graph data and introduce massive noise. More importantly, for fixed corpus-level graph structure, these models cannot sufficiently exploit the labeled and unlabeled information of nodes. Meanwhile, contrastive learning has been developed as an effective method in graph domain to fully utilize the information of nodes. Therefore, we propose a new graph-based model for text classification named CGA2TC, which introduces contrastive learning with an adaptive augmentation strategy into obtaining more robust node representation. First, we explore word co-occurrence and document word relationships to construct a text graph. Then, we design an adaptive augmentation strategy for the text graph with noise to generate two contrastive views that effectively solve the noise problem and preserve essential structure. Specifically, we design noise-based and centrality-based augmentation strategies on the topological structure of text graph to disturb the unimportant connections and thus highlight the relatively important edges. As for the labeled nodes, we take the nodes with same label as multiple positive samples and assign them to anchor node, while we employ consistency training on unlabeled nodes to constrain model predictions. Finally, to reduce the resource consumption of contrastive learning, we adopt a random sample method to select some nodes to calculate contrastive loss. The experimental results on several benchmark datasets can demonstrate the effectiveness of CGA2TC on the text classification task. 相似文献
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为了提高电子商务推荐系统的精度,提出了基于关联集合的协同过滤推荐算法,该算法通过频繁项集生成算法生成一系列频繁项集,然后通过合并处理过滤掉与用户关联很小的一些噪音项目,从而使协同过滤算法更加有效。该算法在推荐精度上比传统的方法优越。 相似文献
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随着移动互联网的发展,越来越多的用户信息获取过程通过移动终端完成.但当前个性化推荐系统对用户情境的感知能力不足,缺乏为用户提供符合当前情境的个性化信息推荐服务.为此,本文提出了基于贝叶斯方法的情境化用户资源类别偏好学习以及融合该类别偏好的协同过滤个性化信息推荐.运用贝叶斯方法学习用户在不同情境下对各资源类别的偏好,然后将该类别偏好与传统协同过滤推荐算法相结合,生成符合用户当前情境的个性化信息推荐.实验表明本文提出的改进算法可以提高推荐的准确率. 相似文献
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《Information processing & management》2023,60(3):103256
Music has a close relationship with people's emotion and mental status. Music recommendation has both economic and social benefits. Unfortunately, most existing music recommendation methods were constructed based on genre features (e.g., style and album), which cannot meet the emotional needs of listeners. Furthermore, the “filter bubble” effect may make the situation even worse, when a user seeks music for emotional support. In this study, we designed a novel emotion-based personalized music recommendation framework to meet users’ emotional needs and help improve their mental status. In our framework, we designed a LSTM-based model to select the most suitable music based on users’ mood in previous period and current emotion stimulus. A care factor was used to adjust the results so that users’ mental status could be improved by the recommendation. The empirical experiments and user study showed that the recommendations of our novel framework are precise and helpful for users. 相似文献
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《Information processing & management》2020,57(6):102304
The way that users provide feedback on items regarding their satisfaction varies among systems: in some systems, only explicit ratings can be entered; in other systems textual reviews are accepted; and in some systems, both feedback types are accommodated. Recommender systems can readily exploit explicit ratings in the rating prediction and recommendation formulation process, however textual reviews -which in the context of many social networks are in abundance and significantly outnumber numeric ratings- need to be converted to numeric ratings. While numerous approaches exist that calculate a user's rating based on the respective textual review, all such approaches may introduce errors, in the sense that the process of rating calculation based on textual reviews involves an uncertainty level, due to the characteristics of the human language, and therefore the calculated ratings may not accurately reflect the actual ratings that the corresponding user would enter. In this work (1) we examine the features of textual reviews, which affect the reliability of the review-to-rating conversion procedure, (2) we compute a confidence level for each rating, which reflects the uncertainty level for each conversion process, (3) we exploit this metric both in the users’ similarity computation and in the prediction formulation phases in recommender systems, by presenting a novel rating prediction algorithm and (4) we validate the accuracy of the presented algorithm in terms of (i) rating prediction accuracy, using widely-used recommender systems datasets and (ii) recommendations generated for social network user satisfaction and precision, where textual reviews are abundant. 相似文献
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With the expansion of information on the web, recommendation systems have become one of the most powerful resources to ease the task of users. Traditional recommendation systems (RS) suggest items based only on feedback submitted by users in form of ratings. These RS are not competent to deal with definite user preferences due to emerging and situation dependent user-generated content on social media, these situations are known as contextual dimensions. Though the relationship between contextual dimensions and user’s preferences has been demonstrated in various studies, only a few studies have explored about prioritization of varying contextual dimensions. The usage of all contextual dimensions unnecessary raises the computational complexity and negatively influences the recommendation results. Thus, the initial impetus has been made to construct a neural network in order to determine the pertinent contextual dimensions. The experiments are conducted on real-world movies data-LDOS CoMoDa dataset. The results of neural networks demonstrate that contextual dimensions have a significant effect on users’ preferences which in turn exerts an intense impact on the satisfaction level of users. Finally, tensor factorization model is employed to evaluate and validate accuracy by including neural network’s identified pertinent dimensions which are modeled as tensors. The result shows improvement in recommendation accuracy by a wider margin due to the inclusion of the pertinent dimensions in comparison to irrelevant dimensions. The theoretical and managerial implications are discussed. 相似文献
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Fattane Zarrinkalam Mohsen Kahani Ebrahim Bagheri 《Information processing & management》2018,54(2):339-357
Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users. 相似文献
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《Information processing & management》2016,52(3):459-477
Nowadays, the increasing demand for group recommendations can be observed. In this paper we address the problem of recommendation performance for groups of users (group recommendation). We focus on the performance of very Top-N recommendations, which are important when recommending the long lasting items (only a few such items are consumed per session, e.g. movie). To improve existing group recommenders we propose a mixed hybrid recommender for groups combining content-based and collaborative strategies. The principle of proposed group recommender is to generate content and collaborative recommendations for each user, apply an aggregation strategy to solve the group conflict preferences for the content and collaborative sets separately, and finally reorder the collaborative candidates based on the content-based ones. It is based on an idea that candidates recommended by both recommendation strategies at the same time are presumably more appropriate for the group than the candidates recommended by individual strategies. The evaluation is performed by several experiments in the multimedia domain (as typical representative for group recommendations). Both, online and offline experiments were performed in order to compare real users’ satisfaction to the standard group recommenders and also, to compare performance of proposed approach to the state-of-the-art recommenders based on the MovieLens dataset. Finally, we experimented with the proposed hybrid recommender to generate the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation for groups of users and for the single-user recommendation respectively on very Top-N recommended items. 相似文献
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Sajad Ahmadian Majid Meghdadi Mohsen Afsharchi 《Information processing & management》2018,54(4):707-725
Recommender systems are techniques to make personalized recommendations of items to users. In e-commerce sites and online sharing communities, providing high quality recommendations is an important issue which can help the users to make effective decisions to select a set of items. Collaborative filtering is an important type of the recommender systems that produces user specific recommendations of the items based on the patterns of ratings or usage (e.g. purchases). However, the quality of predicted ratings and neighbor selection for the users are important problems in the recommender systems. Selecting suitable neighbors set for the users leads to improve the accuracy of ratings prediction in recommendation process. In this paper, a novel social recommendation method is proposed which is based on an adaptive neighbor selection mechanism. In the proposed method first of all, initial neighbors set of the users is calculated using clustering algorithm. In this step, the combination of historical ratings and social information between the users are used to form initial neighbors set for the users. Then, these neighbor sets are used to predict initial ratings of the unseen items. Moreover, the quality of the initial predicted ratings is evaluated using a reliability measure which is based on the historical ratings and social information between the users. Then, a confidence model is proposed to remove useless users from the initial neighbors of the users and form a new adapted neighbors set for the users. Finally, new ratings of the unseen items are predicted using the new adapted neighbors set of the users and the interested items are recommended to the active user. Experimental results on three real-world datasets show that the proposed method significantly outperforms several state-of-the-art recommendation methods. 相似文献
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《Information processing & management》2020,57(6):102337
A large volume of data flowing throughout location-based social networks (LBSN) gives support to the recommendation of points-of-interest (POI). One of the major challenges that significantly affects the precision of recommendation is to find dynamic spatio-temporal patterns of visiting behaviors, which can hardly be figured out because of the multiple side factors. To confront this difficulty, we jointly study the effects of users’ social relationships, textual reviews, and POIs’ geographical proximity in order to excavate complex spatio-temporal patterns of visiting behaviors when the data quality is unreliable for location recommendation in spatio-temporal social networks. We craft a novel framework that recommends any user the POIs with effectiveness. The framework contains two significant techniques: (i) a network embedding method is adopted to learn the vectors of users and POIs in an embedding space of low dimension; (ii) a dynamic factor graph model is proposed to model various factors such as the correlation of vectors in the previous phase. A collection of experiments was carried out on two real large-scale datasets, and the experimental outcomes demonstrate the supremacy of the proposed method over the most advanced baseline algorithms owing to its highly effective and efficient performance of POI recommendation. 相似文献
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《Information processing & management》2023,60(4):103403
Most of the existing GNN-based recommender system models focus on learning users’ personalized preferences from these (explicit/implicit) positive feedback to achieve personalized recommendations. However, in the real-world recommender system, the users’ feedback behavior also includes negative feedback behavior (e.g., click dislike button), which also reflects users’ personalized preferences. How to utilize negative feedback is a challenging research problem. In this paper, we first qualitatively and quantitatively analyze the three kinds of negative feedback that widely existed in real-world recommender systems and investigate the role of negative feedback in recommender systems. We found that it is different from what we expected — not all negative items are ranked low, and some negative items are even ranked high in the overall items. Then, we propose a novel Signed Graph Neural Network Recommendation model (SiGRec) to encode the users’ negative feedback behavior. Our SiGRec can learn positive and negative embeddings of users and items via positive and negative graph neural network encoders, respectively. Besides, we also define a new Sign Cosine (SiC) loss function to adaptively mine the information of negative feedback for different types of negative feedback. Extensive experiments on four datasets demonstrate the proposed model outperforms several existing models. Specifically, on the Zhihu dataset, SiGRec outperforms the unsigned GNN model (i.e., LightGCN), 27.58% 29.81%, and 31.21% in P@20, R@20, and nDCG@20, respectively. We hope our work can open the door to further exploring the negative feedback in recommendations. 相似文献