101. Proactive Recommendation Based on $$\mathcal{E}\mathcal{L}$$ Concept Learning
- Author
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Jianfeng Du, Yong Hu, Xiaoli Yao, Bohong Lin, and Shuai Wang
- Subjects
Set (abstract data type) ,Discrete mathematics ,Integer ,Concept learning ,Maximal set ,Mathematics - Abstract
This paper proposes a novel knowledge-based approach to proactive recommendation. It exploits \(\mathcal{E}\mathcal{L}\) concept learning to automatically model user preferences and is different from traditional knowledge-based approaches that require users to input explicit needs. Given an item being browsed, a set of marked items and an integer threshold k which is used to determine user preferences (where individuals are treated as items), the approach learns recommendatory restricted \(\mathcal{E}\mathcal{L}\) concepts and returns unmarked instances of these concepts as recommendations. A recommendatory restricted \(\mathcal{E}\mathcal{L}\) concept is a most specific restricted \(\mathcal{E}\mathcal{L}\) concept that has at least k marked items, the item being browsed and at least one other unmarked item as instances. Intuitively, a recommendatory restricted \(\mathcal{E}\mathcal{L}\) concept models a maximal set of user preferences. To guarantee that a learned concept has a finite size and the learning process is efficient, the proposed approach does not handle general \(\mathcal{E}\mathcal{L}\) concepts but only restricted \(\mathcal{E}\mathcal{L}\) concepts which have restrictions on the number of nested quantifiers and the inner occurrence of existential restrictions. This paper treats the problem of learning recommendatory restricted \(\mathcal{E}\mathcal{L}\) concepts as the problem of maximal frequent itemset mining (MFI-mining) and presents an efficient MFI-mining algorithm to learn these concepts. Experimental results demonstrate the feasibility of the proposed approach in terms of efficiency and scalability.
- Published
- 2013