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Recommender systems promise to support travelers in complex decision-making processes; however, whether a recommendation is seen as credible advice and actually taken into account not only depends on travelers' perceptions of the ...
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Recommender systems promise to support travelers in complex decision-making processes; however, whether a recommendation is seen as credible advice and actually taken into account not only depends on travelers' perceptions of the recommendation but also of the system as the advice giver. A scale to measure recommender system credibility was developed and tested. The results confirm that credibility has two dimensions: expertise and trustworthiness. Further, significant gender differences in credibility perceptions were found. The findings also indicate that respondents prefer humans as recommendation sources and that this preference is influenced by perceptions of lack of credibility of recommender systems as well as gender-specific preferences. Implications for future research and for recommender system design are discussed.
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We propose a method to determine the credibility of messages that are posted in participatory media (such as blogs), of use in recommender systems designed to provide users with messages that are considered to be the most credible...
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We propose a method to determine the credibility of messages that are posted in participatory media (such as blogs), of use in recommender systems designed to provide users with messages that are considered to be the most credible to them. Our approach draws from theories developed in sociology, political science, and information science-we show that the social context of users influences their opinion about the credibility of messages they read, and that this context can be captured by analyzing the social network of users. We use this insight to improve recommendation algorithms for messages created in participatory media environments. Our methodology rests on Bayesian learning, integrating new concepts of context and completeness of messages inspired by the strength of weak ties hypothesis from social network theory. We show that our credibility evaluation model can be used to significantly enhance the performance of collaborative filtering recommendation. Experimental validation is done using datasets obtained from social networking websites used for knowledge sharing. We conclude by clarifying our relationship to the semantic adaptive social web, emphasizing our use of personal evaluations of messages and the social network of users, instead of merely automated semantic interpretation of content.
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摘要 :
We propose a method to determine the credibility of messages that are posted in participatory media (such as blogs), of use in recommender systems designed to provide users with messages that are considered to be the most credible...
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We propose a method to determine the credibility of messages that are posted in participatory media (such as blogs), of use in recommender systems designed to provide users with messages that are considered to be the most credible to them. Our approach draws from theories developed in sociology, political science, and information science—we show that the social context of users influences their opinion about the credibility of messages they read, and that this context can be captured by analyzing the social network of users. We use this insight to improve recommendation algorithms for messages created in participatory media environments. Our methodology rests on Bayesian learning, integrating new concepts of context and completeness of messages inspired by the strength of weak ties hypothesis from social network theory. We show that our credibility evaluation model can be used to significantly enhance the performance of collaborative filtering recommendation. Experimental validation is done using datasets obtained from social networking websites used for knowledge sharing. We conclude by clarifying our relationship to the semantic adaptive social web, emphasizing our use of personal evaluations of messages and the social network of users, instead of merely automated semantic interpretation of content.
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Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user interest profiling with reviewer cr...
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Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules namely candidate feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment and recommendation module. Review corpus is given as an input to the CISER model. Candidate feature extraction module uses context and sentiment confidence to extract features of importance. To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their opinion according to their credibility. The user interest mining module uses aesthetics of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module scores candidate features present in review based on their fastText sentiment polarity. Finally, the recommendation module uses credibility weighted sentiment scoring of user preferred features for purchase recommendations. The proposed recommendation methodology harnesses not only numeric ratings, but also sentiment expressions associated with features, customer preference profile and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93% and MAP@3 is 49%, which is better than current state-of-the-art systems.
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Collaborative filtering (CF) is the most commonly applied recommendation system for personalized services. Since CF systems rely on neighbors as information sources, the recommendation quality of CF depends on the recommenders sel...
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Collaborative filtering (CF) is the most commonly applied recommendation system for personalized services. Since CF systems rely on neighbors as information sources, the recommendation quality of CF depends on the recommenders selected. However, conventional CF has some fundamental limitations in selecting neighbors: recommender reliability proof, theoretical lack of credibility attributes, and no consideration of customers' heterogeneous characteristics. This study employs a multidimensional credibility model, source credibility from consumer psychology, and provides a theoretical background for credible neighbor selection. The proposed method extracts each consumer's importance weights on credibility attributes, which improves the recommendation performance by personalizing recommendations.
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There have been many research efforts aimed at improving recommendation accuracy with Collaborative Filtering (CF). Yet there is still a lack of investigation into the integration of CF algorithms with the analysis of users' ratin...
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There have been many research efforts aimed at improving recommendation accuracy with Collaborative Filtering (CF). Yet there is still a lack of investigation into the integration of CF algorithms with the analysis of users' rating behaviors. In this work, we develop an integrated CF-based recommendation solution by incorporating the credibility of users' ratings, the demographic information of people, and the ontological semantics of items. The users' credibility values are calculated based on their ratings and they are used in finding credible neighbors to improve the accuracy of recommendations. The demographic information and ontological semantics are used in the similarity measurement of users/items to alleviate the issues of sparsity and cold start in CF algorithms. Experiments are conducted on real-world datasets of MovieLens and Yahoo!Movie. Compared with baseline methods, a set of experiments shows that the proposed approach improves the recommendation quality significantly.
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Recommender system has been emerged as a personalization tool to solve the issue of information overload in an e-commerce environment. Traditional collaborative filtering (CF) based recommender systems (RSs) suggest items to users...
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Recommender system has been emerged as a personalization tool to solve the issue of information overload in an e-commerce environment. Traditional collaborative filtering (CF) based recommender systems (RSs) suggest items to users based on their overall ratings which are used to find out similar users. Multi-criteria ratings are used to capture user preferences efficiently in multi-criteria recommender systems (MCRS), and incorporation of various criteria ratings can lead to higher performance in MCRS. Usually, user relies on the credibility of an item provided through his/her social circle or similar users, which is called a personal view on items from their close ones. However, it is not generally sufficient to depend exclusively on the personal view of the user. Therefore, public view that includes whole community can play a key role in the credibility of an item. In this paper, we propose a MCRS based on the credibility score of an item, which is an aggregated value of credibility scores on various criteria of an item. These credibility scores are computed based on personal and public views. However, different users have different priorities to various criteria of an item. Therefore, we use genetic algorithm (GA) to learn appropriate weights in the aggregation task of credibility score. The experiment results on Yahoo! Movies and modified MovieLens dataset demonstrate the effectiveness of proposed credibility score based MCRS in terms of coverage, recall, precision, and f-measure. (C) 2020 Elsevier B.V. All rights reserved.
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摘要 :
Recommender system has been emerged as a personalization tool to solve the issue of information overload in an e-commerce environment. Traditional collaborative filtering (CF) based recommender systems (RSs) suggest items to users...
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Recommender system has been emerged as a personalization tool to solve the issue of information overload in an e-commerce environment. Traditional collaborative filtering (CF) based recommender systems (RSs) suggest items to users based on their overall ratings which are used to find out similar users. Multi-criteria ratings are used to capture user preferences efficiently in multi-criteria recommender systems (MCRS), and incorporation of various criteria ratings can lead to higher performance in MCRS. Usually, user relies on the credibility of an item provided through his/her social circle or similar users, which is called a personal view on items from their close ones. However, it is not generally sufficient to depend exclusively on the personal view of the user. Therefore, public view that includes whole community can play a key role in the credibility of an item. In this paper, we propose a MCRS based on the credibility score of an item, which is an aggregated value of credibility scores on various criteria of an item. These credibility scores are computed based on personal and public views. However, different users have different priorities to various criteria of an item. Therefore, we use genetic algorithm (GA) to learn appropriate weights in the aggregation task of credibility score. The experiment results on Yahoo! Movies and modified MovieLens dataset demonstrate the effectiveness of proposed credibility score based MCRS in terms of coverage, recall, precision, and f-measure. (C) 2020 Elsevier B.V. All rights reserved.
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With the rapid development of service-oriented computing, cloud computing and big data, a large number of functionally equivalent web services are available on the Internet. Quality of Service (QoS) becomes a differentiating point...
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With the rapid development of service-oriented computing, cloud computing and big data, a large number of functionally equivalent web services are available on the Internet. Quality of Service (QoS) becomes a differentiating point of services to attract customers. Since the QoS of services varies widely among users due to the unpredicted network, physical location and other objective factors, many Collaborative Filtering based approaches are recently proposed to predict the unknown QoS by employing the historical user-contributed QoS data. However, most existing approaches ignore the data credibility problem and are thus vulnerable to the unreliable QoS data contributed by dishonest users. To address this problem, we propose a trust-aware approach TAP for reliable personalized QoS prediction. Firstly, we cluster the users and calculate the reputation of users based on the clustering information by a beta reputation system. Secondly, a set of trustworthy similar users is identified according to the calculated user reputation and similarity. Finally, we identify a set of similar services by clustering the services and make prediction for active users by combining the QoS data of the trustworthy similar users and similar services. Comprehensive real-world experiments are conducted to demonstrate the effectiveness and robustness of our approach compared with other state-of-the-art approaches. (C) 2016 Elsevier B.V. All rights reserved.
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Recommender systems (RSs) have recently gained significant attention from both research and industrial communities. These systems generate the recommendations of items in one of two ways, namely collaborative or content-based filt...
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Recommender systems (RSs) have recently gained significant attention from both research and industrial communities. These systems generate the recommendations of items in one of two ways, namely collaborative or content-based filtering. Collaborative filtering is a technique used by recommender systems in order to suggest to the user a set of items based on the opinions of other users who share with him the same preferences. One of the key issues in collaborative filtering systems (CFSs) is how to generate adequate recommendations for newcomers who rate only a small number of items, a problem known as cold start user. Another interesting problem is the cold start item when a new item is introduced in the system and cannot be recommended. In this paper, we present a clustering-based approach SCOL that aims to alleviate the cold start challenges; by identifying the most effective opinion leaders among the social network of the CFS. SCOL clustering focuses on the credibility and correlation similarity concepts.
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