An Online Personalized Recommendation Model Based on Bayesian Networks

Zhang, Shaozhong; Liu, Lu

doi:10.1007/978-0-387-76312-5_91

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Movielens [12], BooksCrossing [13] and Netflix [14] have been built with the sole purpose of supporting research activities. Technologies that have been applied to RS include Nearest-neighbor (which includes Collaborative filtering), Bayesian networks [15] and Clustering [16]. Bayesian networks create a decision tree based on a set of user ratings.…”

Section: Motivationmentioning

confidence: 99%

Trust-Enhanced Recommender Systems for Efficient On-Line Collaboration

Pitsilis

2009

Trust Management III

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Abstract. Trust has been explored by many researchers in the past as a solution for assisting the process of recommendation production. In this work we are examining the feasibility of building networks of trusted users using the existing evidence that would be provided by a standard recommender system. As there is lack of models today that could help in finding the relationship between trust and similarity we build our own that uses a set of empirical equations to map similarity metrics into Subjective Logic trust. In this paper we perform evaluation of the proposed model as being a part of a complete recommender system. Finally, we present the interesting results from this evaluation that shows the performance and benefits of our trust modeling technique as well as its impact on the user community as it evolves over time.

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Section: Motivationmentioning

confidence: 99%

Trust-Enhanced Recommender Systems for Efficient On-Line Collaboration

Pitsilis

2009

Trust Management III

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Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment

Sundari

Subaji

2020

Int. J. Info. Tech. Dec. Mak.

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Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications

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An Online Personalized Recommendation Model Based on Bayesian Networks

Cited by 2 publications

References 7 publications

Trust-Enhanced Recommender Systems for Efficient On-Line Collaboration

Trust-Enhanced Recommender Systems for Efficient On-Line Collaboration

Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment

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