User Preference Based Recommendation System Design with Adaptive Concept Space
Date Issued
2014
Date
2014
Author(s)
Wu, Jie-Wei
Abstract
This thesis proposes a recommendation system (RS) which incorporates the advantages of the user/item-based collaborative filtering (CF) and the content-based filtering.
Unlike the user/item-based CF where the user/item spaces are of high dimension, the proposed RS utilizes the user-based and item-based concept spaces where dimension, or the number of concepts, is increased only necessary.
In addition, the proposed system can deal with the cold start problem with producing another kind dimension of items.
With modifying clustering results, it can be used to create recommendation in the rapid increasing information.
The dimension of the item-based concepts is defined by the features of the items, and concepts are the clustering result of the item-based concept space.
The user-based concepts are the result of clustering adjustment from the item-based concepts with the information of users'' behaviors, such as whether or not a user is interested in both items in a concept.
The user-base and item-based concepts co-evolve iteratively in the above manner.
At the end, the proposed RS utilizes the learned concepts combined with the reading dependence to perform recommendation.
The proposed techniques are demonstrated on the article recommendation.
In this case, the features of an item correspond to the segmented contents of an article, and users'' behaviors correspond to users'' reading preferences.
In the experiment, the item-based/user-based CF dimension is about $30,000$ and $3,000$ while the concept space in proposed RS articles starts from $5$ and ended up merely $87$ after $12$ iterations.
The proposed RS dynamically adjust the dimension of articles.
The dimensions of articles is $44$ in the end and used for clustering articles.
New articles then can be clustered and recommended as well.
The precision-recall curves indicates that the proposed RS achieves more hits than user-based/item-based CF and content-based filtering.
The average precision-recall curves and mean average precision of proposed system grows and exceeds others.
This idea of two concept spaces can be extended to the situation with items with extractable features as dimension and the interaction between items and users.
Subjects
推薦系統
協同式過濾
內容式資訊過濾
使用者閱讀行為
Type
thesis
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