Item recommender system by incorporating metadata information into ternary semantic analysis
Pages 670 - 675
Abstract
In the web world, there are massive data-items that are readily available, and it has become a tedious job to identify needed items for users. Therefore, there is a need of recommender system that analyzes the user's behavior and accordingly recommend the items. Existing algorithm uses cubic analysis approach to grab three-way correlations like user-item-tag or user-item-rating. This analysis totally relies on ratings provided by users, but it does not use at-all user-item-tag profile for the analysis, hence does not take full advantage of user's transaction history. This paper proposes an approach to incorporate profile (meta-data) information of user-tag-item for analysis along with cubic approach. This lead to a proper understanding of user, and improving the quality of recommendations. Moreover, the major issue in the recommendation is sparsity, i.e., most of the entries in the tensor remain zero, so it is worth to store only non-zero values. Hence, we use co-ordinate format approach to store and access data into a tensor. The solution has been evaluated by applying MovieLens data set in the proposed approach, and the result is caused by computing precision against top-N recommended items. Since, the proposed approach has incorporated meta-data information, the result generated is better than the existing solution.
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Index Terms
- Item recommender system by incorporating metadata information into ternary semantic analysis
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Information
Published In
September 2012
879 pages
ISBN:9781450311854
DOI:10.1145/2381716
- General Chair:
- Vidyasagar Potdar,
- Program Chair:
- Debajyoti Mukhopadhyay
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Published: 03 September 2012
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