Learning over Knowledge-Base Embeddings for Recommendation

State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines

Trust and Distrust in Big Data Recommendation Agents

Big data technology allows for managing data from a variety of sources, in large amounts, and at a higher velocity than before, impacting several traditional systems, including recommendation agents. Along with these improvements, there are concerns about trust and distrust in RA recommendations. Much prior work on trust has been done in IS, but only a few have examined trust and distrust in the context of big data and analytics. In this vein, the purpose of this study is to study the eight antecedents of trust and distrust in recommendation agents’ cues in the context of the Big Data ecosystem using an experiment. Our study contributes to the literature by integrating big data and recommendation agent IT artifacts, expanding trust and distrust theory in the context of a big data ecosystem, and incorporating the constructs of algorithm innovativeness and process transparency