UBC Theses and Dissertations
Composite recommendation : semantics and efficiency Xie, Min
Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, many applications can benefit from a system which is capable of recommending packages of items. Sample applications include travel planning, e-commerce, and course recommendation. In these contexts, there is a need for a system that can recommend the most relevant packages for the user to choose from. In this thesis we highlight our research achievements for the composite recommendation problem. We first consider the problem of composite recommendation under hard constraint, e.g., budget. It is clear that this is a very common paradigm for the composite recommendation problem. In Chapter 3, we first discuss how given a fixed package schema, we can efficiently find the top-k most relevant packages with hard constraints. The proposed algorithm is shown to be instance optimal, which means that no algorithm in a reasonable class can perform more than a constant times better, for some fixed constant. And we also propose relaxed solutions based on probabilistic reasoning. In Chapter 4, we lift the constraint on the package schema, and discuss how efficient algorithms can be derived to solve the more general problem with a flexible package schema. For this problem, again we propose both instance optimal algorithm and heuristics-based solution which have been verified to be effective and efficient through our extensive empirical study. Then in Chapter 5, motivated by the fact that hard constraints sometimes might lead to unfavorable results, and following the recent paradigm on “softening” the constraints, we study the problem of how to handle top-k query processing with soft constraints. Finally, in Chapter 6, we discuss a general performance tuning solution based on cached views which can be leveraged to further optimize the various algorithms proposed in this thesis.
Item Citations and Data
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