SSL-STR: Semi-Supervised Learning for Sparse Trust Recommendation | IEEE Conference Publication | IEEE Xplore

SSL-STR: Semi-Supervised Learning for Sparse Trust Recommendation


Abstract:

Trust is widely applied in recommender systems to improve recommendation performance by alleviating well-known problems, such as cold start, data sparsity, and so on. How...Show More

Abstract:

Trust is widely applied in recommender systems to improve recommendation performance by alleviating well-known problems, such as cold start, data sparsity, and so on. However, trust data itself also faces sparse problems. To solve these problems, we propose a novel sparse trust recommendation model, SSL-STR. Specifically, we decompose the aspects influencing trust-building into finer-grained factors, and combine these factors to mine the implicit sparse trust relationships among users by employing the Transductive Support Vector Machine algorithm. Then we extend SVD++ model with social trust and sparse trust information for rating prediction in the recommendation system. Experiments show that our SSL-STR improves the recommendation accuracy by up to 4.3%.
Date of Conference: 09-13 December 2019
Date Added to IEEE Xplore: 27 February 2020
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Conference Location: Waikoloa, HI, USA

References

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