کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4946216 1364091 2017 7 صفحه PDF ندارد دانلود کنید
عنوان انگلیسی مقاله
Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding
چکیده انگلیسی

In this paper, we focus on the problem of context-aware recommendation using tensor factorization. Traditional tensor-based models in context-aware recommendation scenario only consider user-item-context interactions. In this paper, we argue that rating can't be totally explained by the interactions and the rating also influenced by the combined impact of overall mean, user bias, item bias and context bias. Based on this hypothesis, we propose a novel context-aware recommendation model named Bias Tensor Factorization, which take all this factors into account. Additionally, traditional context-aware recommenders with tensor factorization still have three main drawbacks: (1) the model complexity of those models increase exponentially with the number of context features, (2) those models can only handle context features with categorical values and (3) the models fail to select effective features from available context features. To address those problems, we propose a context features auto-encoding algorithm based on regression tree which can both handle numerical features and select effective features. Then we integrate this algorithm with Bias Tensor Factorization. Experiments on a real world contextual dataset and Movielens show that our proposed algorithms outperform the state-of-art context-aware recommendation algorithms, namely tensor factorization and factorization machine.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 128, 15 July 2017, Pages 71-77
نویسندگان
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