Identifying Influential Taggers in Trust-Aware Recommender Systems
26 Aug 2012-pp 1284-1288
TL;DR: This paper purpose an approach to identify influential taggers in a trust based social network so that efforts to encourage tagging can be achieved by designing incentives for motivating the influentialtaggers to apply more tags.
Abstract: Trust-ware recommender systems provide the features of personalized product and service recommendations in web based social networks by using the trust connections existing between users and preferences data available for each user. One of the main sources of user preferences data are the tags that users apply to different items. Encouraging users to apply more tags is one of the challenges faced by most social network sites. In this paper we purpose an approach to identify influential taggers in a trust based social network so that efforts to encourage tagging can be achieved by designing incentives for motivating the influential taggers to apply more tags. In our proposed approach, for every user his tagging influencer is that user in his personal network who has influenced his tagging behavior the most. We define an active user tagging actions has been influenced by a user in his personal network only when the active user tags an item after his influencer has tagged it. The influential taggers in the overall social network are those who have the influenced the maximum number of users in the network. We analyze the real life dataset of Last.fm to show that our approach is different from the current approach of defining those users who have tagged the maximum number of items as the influential users. We also discuss the implications of using our approach.
Citations
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TL;DR: A social trust-aware system for recommending Web services (WSs) based on social qualities of WSs that they exhibit towards peers at runtime, and trustworthiness of the users who provide feedback on their overall experience using WSs is proposed.
Abstract: Recommender systems have shown great potential to help users find interesting and relevant Web service (WS) from within large registers. However, with the proliferation of WSs, recommendation becomes a very difficult task. Social computing seems offering innovative solutions to overcome those shortcomings. Social computing is at the crossroad of computer sciences and social sciences disciplines by looking into ways of improving application design and development using elements that people encounter daily such as social networks, trust, reputation, and recommendation. In this paper, we propose a social trust-aware system for recommending Web services (WSs) based on social qualities of WSs that they exhibit towards peers at runtime, and trustworthiness of the users who provide feedback on their overall experience using WSs. A set of experiments to assess the fairness and accuracy of the proposed system are reported in the paper, showing promising results and demonstrating that our service recommendation method significantly outperforms conventional similarity-based and trust-based service recommendation methods.
10 citations
06 May 2014
TL;DR: This work used friendship time and proposed a novel temporal-trust based approach called AgeTrust to measure trust value and shows that the proposed approach outperforms the traditional approaches.
Abstract: Recommender systems are useful techniques for solving the problem of information overload. Collaborative Filtering (CF) is the most successful approach for recommendation. This approach focuses on previous indicate preferences which is known for its traditional problems such as cold-start, sparsity and hacking. For solving the problem of hacking and improving the accuracy, trust-based CF methods have been proposed previously. These methods focused on trust values among the users. Nonetheless, most existing approaches use trust as a factor independent from time which we think that trust value between users is dynamic; hence it change over time. For this reason, we used friendship time and proposed a novel temporal-trust based approach called AgeTrust to measure trust value. To validate the proposed approach, we used Delicious data set and compared our approach with two other traditional trust-based approaches: traditional CF and FriendshipTrust. Result shows that our proposed approach outperforms the traditional approaches.
7 citations
Cites background from "Identifying Influential Taggers in ..."
...Other researchers have also used temporal data for modeling the users’ behaviors [21], [22]....
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20 May 2014
TL;DR: This paper argues that trust value between users is dynamic; hence it change over time and proposes a novel temporal-trust based approach to calculate trust values aware of time of friendship.
Abstract: While Collaborative Filtering (CF) recommender systems, which focus on previous indicate preferences, are known for their traditional problems such as cold-start, sparsity and modest accuracy, trust-based CF has been previously proposed to solve such issues by focusing on trust values among the users. Nonetheless, most existing approaches use trust as an independent factor from time, in this paper we argue that trust value between users is dynamic; hence it change over time. For this reason we propose a novel temporal-trust based approach to calculate trust values aware of time of friendship. To validate the proposed approach in this paper, we used Delicious data set and compared our approach with traditional CF and trust-based approaches. Results showed that accuracy of proposed approach overcomes the traditional approaches.
6 citations
Cites background from "Identifying Influential Taggers in ..."
...Other researchers have also used temporal data for modeling the users’ activities [19, 20]....
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01 Dec 2013
TL;DR: This paper proposes a novel pheromone-based approach to calculate trustworthiness by focusing on time factor, which is hoped to reduce cold-start and sparsity as well as improve accuracy of the recommendation results.
Abstract: Collaborative Filtering (CF) is the most successful technology for recommender systems. The technology does not rely on actual content of the items, but instead requires users to indicate preferences, most commonly in the form of ratings. While CF is known for its traditional problems such as cold-start, sparsity and modest accuracy, a trust-based CF has been previously proposed to solve such issues by focusing on trust values among the users. Nonetheless, all existing trust-based approaches use trust as a factor independent from scope, whether explicit or implicit. We argue that trustworthiness should not be the same across all conditions; hence the trust values should change to suit certain scope or focused area. To validate the proposed temporal-focused trustworthiness in this paper, we propose a novel pheromone-based approach to calculate trustworthiness by focusing on time factor. Implementation of the proposed approach is hoped to reduce cold-start and sparsity as well as improve accuracy of the recommendation results.
5 citations
SIDI1
TL;DR: A social trust-aware system for recommending WS based on social qualities of WSs that they exhibit towards peers at run-time, and trustworthiness of the users who provide feedback on their overall experience using WSs is proposed.
Abstract: Recommender systems help users find relevant Web service based on peers' previous experiences dealing with Web services (WSs). However, with the proliferation of WSs, recommendation has become “questionable†. Social computing seems offering innovative solutions to improve the quality of recommendations. Social computing is at the crossroad of computer sciences and social sciences disciplines by looking into ways of improving application design and development using elements that people encounter daily such as collegiality, friendship and trust. In this paper, the authors propose a social trust-aware system for recommending WS based on social qualities of WSs that they exhibit towards peers at run-time, and trustworthiness of the users who provide feedback on their overall experience using WSs. A set of experiments to assess the fairness and accuracy of the proposed system are reported in the paper, showing promising results.
3 citations
References
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01 Apr 2001
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Abstract: Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative ltering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative ltering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between di erent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze di erent item-based recommendation generation algorithms. We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
8,634 citations
26 Aug 2001
TL;DR: It is proposed to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively, taking advantage of the availability of large relevant databases.
Abstract: One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected profit from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered only the intrinsic value of the customer (i.e, the expected profit from sales to her). We propose to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively. Instead of viewing a market as a set of independent entities, we view it as a social network and model it as a Markov random field. We show the advantages of this approach using a social network mined from a collaborative filtering database. Marketing that exploits the network value of customers---also known as viral marketing---can be extremely effective, but is still a black art. Our work can be viewed as a step towards providing a more solid foundation for it, taking advantage of the availability of large relevant databases.
2,886 citations
"Identifying Influential Taggers in ..." refers background in this paper
...In [9], a framework combined with opinion mining techniques is proposed to evaluate the influential power of online reviewers in a social network site Epinions.com....
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01 Aug 1999
2,686 citations
23 Jul 2002
TL;DR: This research optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him, and takes into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost.
Abstract: Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, and using these models to choose the best viral marketing plan. Knowledge-sharing sites, where customers review products and advise each other, are a fertile source for this type of data mining. In this paper we extend our previous techniques, achieving a large reduction in computational cost, and apply them to data from a knowledge-sharing site. We optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him. We take into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost. Our results show the robustness and utility of our approach.
1,759 citations
"Identifying Influential Taggers in ..." refers background in this paper
...In [9], a framework combined with opinion mining techniques is proposed to evaluate the influential power of online reviewers in a social network site Epinions.com....
[...]
19 Oct 2007
TL;DR: This work proposes to replace the step of finding similar users with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight.
Abstract: Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.
1,137 citations