Difference between revisions of "Restaurant Recommendations Based On Review Content"
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== Basic idea == | == Basic idea == | ||
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| + | Current recommendation systems rely on [[collaborative filtering]]. Suppose we want to recommend a product to John. One way is to look for users who share similar rating patterns as John, and use the ratings from these like minded users to recommend a few products to John. Another way would be to build a item-item matrix that determines the similarity between pairs of items. From this matrix, as well as the John's data (ratings, etc), we can try to infer his tastes and recommend similar items. | ||
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| + | For our 11-763 project, we propose a way recommendation system that looks at the text in user reviews. | ||
== Brief summary of method == | == Brief summary of method == | ||
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| + | In typical recommendation problems, we have a set of users <math>U=\{u_1, u_2, ...\}</math>, items (''things'') <math>T = \{t_1, t_2, ...\}</math> and reviews <math>R=\{r_1, r_2, ...\}</math> where each <math>r_i=(u, t, \mathbf{w})</math> for some user, thing and words. | ||
== Dataset == | == Dataset == | ||
Revision as of 23:20, 28 September 2011
Basic idea
Current recommendation systems rely on collaborative filtering. Suppose we want to recommend a product to John. One way is to look for users who share similar rating patterns as John, and use the ratings from these like minded users to recommend a few products to John. Another way would be to build a item-item matrix that determines the similarity between pairs of items. From this matrix, as well as the John's data (ratings, etc), we can try to infer his tastes and recommend similar items.
For our 11-763 project, we propose a way recommendation system that looks at the text in user reviews.
Brief summary of method
In typical recommendation problems, we have a set of users , items (things) and reviews where each for some user, thing and words.
