Handling data sparsity in collaborative filtering using emotion and semantic based features

Moshfeghi, Yashar; Piwowarski, Benjamin; Jose, Joemon M.

doi:10.1145/2009916.2010001

Cited by 79 publications

(49 citation statements)

References 20 publications

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“…Valuable information that is hard to represent by numeric ratings alone can be extracted from these textual reviews. To improve recommendation in the cold-start scenario, Moshfeghi et al [8] explored the incorporation of emotions and semantic spaces. In their collaborative recommender system, information from user reviews and plot summaries is extracted in order to better describe items and users.…”

Section: Related Workmentioning

confidence: 99%

Improving Cold-Start Recommendations with Social-Media Trends and Reputations

Santos¹,

Peleja²,

Martins³

et al. 2017

Advances in Intelligent Data Analysis XVI

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Abstract. In recommender systems, the cold-start problem is a common challenge. When a new item has no ratings, it becomes difficult to relate it to other items or users. In this paper, we address the cold-start problem and propose to leverage on social-media trends and reputations to improve the recommendation of new items. The proposed framework models the long-term reputation of actors and directors, to better characterize new movies. In addition, movies popularity are deduced from social-media trends that are related to the corresponding new movie. A principled method is then applied to infer cold-start recommendations from these social-media signals. Experiments on a realistic time-frame, covering several movie-awards events between January 2014 and March 2014, showed significant improvements over ratings-only and metadata-only based recommendations.

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Section: Related Workmentioning

confidence: 99%

Improving Cold-Start Recommendations with Social-Media Trends and Reputations

Santos¹,

Peleja²,

Martins³

et al. 2017

Advances in Intelligent Data Analysis XVI

View full text Add to dashboard Cite

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“…Propose framework for automatic to tackle problem of cold start problem by considering item related emotions and semantic data. Make final prediction using latent dirichlet and gradient boosted trees by extracting emotions [9].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Another technique is the collaborative filtering algorithm this also predict rating by calculating average rating of items based on the similarity and correlation between user [14]. Aims to recommends based prior user interaction [9]. This system predicts item on the base of previously rated by other users [1].…”

Section: Introductionmentioning

confidence: 99%

Survey on Quality of Service Recommendation

Jadhav¹,

Satpute²

2016

International Journal of Advanced Research in Computer and Comm

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Tremendous popularity of the ecommerce applications increased now days. Numerous amounts of reviews and ratings are available on the sites. Such as comments, reviews description of the local service. This type of information is valuable for the new user who judges the product online to make their decision. Sometimes this information may be having problem to recommend user about product recommendation because of the lack of reviews and rating of particular product. If any product has two rating one 5 and other is 2 they only have two ratings. So while recommending this product just finds average of ratings and show to user. This not provides quality of service of the recommendation. This is not enough to extract public opinions. To provide quality of service to the recommendation have to improve overall evaluation of rating. Find the trust of the user service rating and feature. Extract the overall rating and trust of service by combining the service rating to providing quality of service.

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“…Due to collaborative filtering recommendation algorithm uses the user's score information to predict the non scoring information, it does not need to obtain other domain information, so it has become the most widely used recommendation algorithm. Collaborative filtering technology has some problems such as data sparsity and cold start [2] . Data sparsity mainly cause by those two reasons, the first is when users mark the project they also pay the price, such as privacy leaks and time waste; the second is the rate of growth is often faster than the speed of user experience.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Collaborative Filtering Recommendation Algorithm Based on User Attributes

Su¹,

Li²,

Li³

2017

dtcse

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Abstract. The rapid development of the Internet brings the convenience but also results in "information trek" problem, which is solved by personalized recommendation algorithms. Traditional collaborative filtering algorithms face data sparsity problem. In order to solve those problems, this paper proposes a dynamic adaptive collaborative filtering recommendation algorithm based on user attributes. The algorithm first uses the item properties to build the user preference matrix, and then establishes the user rating preference matrix according to different behavior habits of users. Finally, based on these two matrixes, it prefills and dynamically adjusts the rating matrix. Experimental results show that the algorithm is more accurate in terms of the recommendation accuracy compared with traditional algorithms.

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Handling data sparsity in collaborative filtering using emotion and semantic based features

Cited by 79 publications

References 20 publications

Improving Cold-Start Recommendations with Social-Media Trends and Reputations

Improving Cold-Start Recommendations with Social-Media Trends and Reputations

Survey on Quality of Service Recommendation

Adaptive Collaborative Filtering Recommendation Algorithm Based on User Attributes

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