Categorizing user interests in recommender systems
08 Sep 2010-pp 282-291
TL;DR: The proposed algorithm is based on drifting preference and has been tested with the Yahoo Webscope R4 dataset and has shown significant improvements over the comparable "Sliding Window" algorithm.
Abstract: The traditional method of recommender systems suffers from the Sparsity problem whereby incomplete dataset results in poor recommendations. Another issue is the drifting preference, i.e. the change of the user's preference with time. In this paper, we propose an algorithm that takes minimal inputs to do away with the Sparsity problem and takes the drift into consideration giving more priority to latest data. The streams of elements are decomposed into the corresponding attributes and are classified in a preferential list with tags as "Sporadic", "New", "Regular", "Old" and "Past" - each category signifying a changing preference over the previous respectively. A repeated occurrence of attribute set of interest implies the user's preference for such attribute(s). The proposed algorithm is based on drifting preference and has been tested with the Yahoo Webscope R4 dataset. Results have shown that our algorithm have shown significant improvements over the comparable "Sliding Window" algorithm.
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07 Jan 2013
TL;DR: A unique smoking intervention plan with the help of mobile phones that uses a Case Based Recommender system that resorts to generating finely customized motivational messages depending on the patient profile and delivery of the same via mobile phones is proposed.
Abstract: The possible cause of many life-threatening diseases such as lung cancer and cardiac myopathy lies in the addiction to tobacco. Apart from being responsible for premature deaths, early diseases and reduced immunity, smoking also inhibits physiological and psychological problems in children born from addicted mothers. Though many developed countries have consciously made efforts to curb smoking, for the developing countries, the trend is still on the rise. The promising factor is that there are many people who are willing to quit smoking and are resorting to technology and electronic media for the same. In this paper, we have proposed a unique smoking intervention plan with the help of mobile phones that uses a Case Based Recommender system. Our model resorts to generating finely customized motivational messages depending on the patient profile and delivery of the same via mobile phones.
17 citations
Cites background from "Categorizing user interests in reco..."
...[30] introduced a heuristic that can adopt preferences with the user’s changing behavior....
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24 Jun 2012
TL;DR: A new preference elicitation system that is based on preference from closed user group that has given competitive results over the comparable techniques like sliding window method or collaborative filtering methods in isolation.
Abstract: Recommender Systems, in order to recommend correctly, demand huge information related to the past transactions and behavior of the user. In the events, where the data is inconsistent or sparse, the systems show a decline in its predictions or recommendations. Here we propose a new preference elicitation system that is based on preference from closed user group. The implicit behavior of the user is tracked when the user picks up an item. The explicit behavior is tracked by the user-ratings for the given item. The user- preference is computed on a memory-based model taking in account the implicit behavior. The peers are identified based on user-similarity on the explicit-preference indicator. The peer preferences are used on the test-dataset to find the percentage of preference that could be matched. The algorithm has been tested on MovieLens dataset and has given competitive results over the comparable techniques like sliding window method or collaborative filtering methods in isolation.
2 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
01 Aug 1999
2,686 citations
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
"Categorizing user interests in reco..." refers background in this paper
...In [4] decreasing weights are assigned to old data....
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17 May 2004
TL;DR: Four open questions are explored that may affect the effectiveness of shilling attacks on recommender systems: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked.
Abstract: Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items can often help a customer find what she is interested in and, therefore can help drive sales. Unscrupulous producers in the never-ending quest for market penetration may find it profitable to shill recommender systems by lying to the systems in order to have their products recommended more often than those of their competitors. This paper explores four open questions that may affect the effectiveness of such shilling attacks: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked. The questions are explored experimentally on a large data set of movie ratings. Taken together, the results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems.
639 citations
25 Oct 2004
TL;DR: An empirical evaluation on Epinions.com dataset shows that trust propagation can increase the coverage of Recommender Systems while preserving the quality of predictions.
Abstract: Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation can increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improvements are achieved for users who provided few ratings.
636 citations
"Categorizing user interests in reco..." refers background or methods in this paper
...Recommender systems are a widely researched area [3], prediction problem and security issues being the more popular issues worked upon....
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...There are two primary approaches that are used to build collaborative filtering memory based recommender systems: user based collaborative filtering [1] and item based collaborative filtering [3]....
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