In recent years, context information has been widely used in recommender systems. Tensor factorization is an effective method to process high-dimensional information. However, data sparsity is more serious in tensor factorization, and it is difficult to build a more accurate recommender system only based on user-item-context interaction information. Making full use of user’s social information and implicit feedback can alleviate this problem. In this paper, we propose a new tensor factorization model named TrustTF, which mainly works as follows: (1) Using user’s social trust information and implicit feedback to extend the bias tensor factorization (BiasTF), effectively alleviate data sparsity problem and improve the recommendation accuracy; (2) Dividing user’s trust relationship into unilateral trust and mutual trust, which makes better use of user’s social information. To our knowledge, this is the first work to consider the effects of both user trust and implicit feedback on the basis of the BiasTF model. The experimental results in two real-world data sets demonstrate that the TrustTF proposed in this paper can achieve higher accuracy than BiasTF and other social recommendation methods.

近年来，上下文信息已在推荐系统中广泛使用。张量分解是处理高维信息的有效方法。但是，数据稀疏性在张量分解中更为严重，并且仅基于用户-项目-上下文交互信息很难构建更准确的推荐系统。充分利用用户的社交信息和隐式反馈可以缓解此问题。本文提出了一种新的张量因子分解模型TrustTF，主要工作如下：（1）利用用户的社会信任信息和隐式反馈来扩展偏量张量因子分解（BiasTF），有效缓解数据稀疏性问题并提高推荐水平准确性; （2）将用户的信任关系分为单方信任和相互信任，更好地利用用户的社交信息。据我们所知，这是在BiasTF模型的基础上考虑用户信任和隐式反馈的影响的第一项工作。在两个实际数据集中的实验结果表明，与BiasTF和其他社交推荐方法相比，本文提出的TrustTF可以实现更高的准确性。