In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

Research on recommender systems (RSs) has traditionally focused on the design of
systems capable of suggesting items of interest for users. However, often the most important …

Recommendation with multiple objectives is an important but difficult problem, where the
coherent difficulty lies in the possible conflicts between objectives. In this case, multi …

Many of today's online services provide personalized recommendations to their users. Such
recommendations are typically designed to serve certain user needs, eg, to quickly find …

In a modern e-commerce recommender system, it is important to understand the
relationships among products. Recognizing product relationships—such as complements or …

Recently, one critical issue looms large in the field of recommender systems–there are no
effective benchmarks for rigorous evaluation–which consequently leads to unreproducible …

In the society utility is the vital concept, especially in mathematical economics. It is
considered as the tendency of an object or action that increases or decreases overall …

In this paper, the method of Lagrange multipliers is used to investigate the utility function;
subject to two constraints: budget constraint, and coupon constraint, and to verify that the …

Existing recommendation algorithms mostly focus on optimizing traditional recommendation
measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-k …

In this paper sensitivity analysis between Lagrange multipliers and the total budget is
discussed. The method of Lagrange multipliers is a very useful and powerful technique in …