Difference between revisions of "Restaurant Recommendations Based On Review Content"
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For our 11-763 project, we propose a way recommendation system that looks at the text in user reviews. | For our 11-763 project, we propose a way recommendation system that looks at the text in user reviews. | ||
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| + | == Assumptions == | ||
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| + | # The text in a user's review will reflect his personal tastes and preferences in restaurants. For instance, he might mention his favorite food, be particular about the service or tend to talk about the ambiance of the restaurant. | ||
| + | # By looking at all the reviews for a specific restaurant, we can infer the strengths/weaknesses of the restaurant. For instance, if many reviews talk about the excellent service, we can use this knowledge in our recommendation system. | ||
== Brief summary of method == | == Brief summary of method == | ||
| − | + | For our problem, we assume that we have a set of users <math>U=\{u_1, u_2, ...\}</math>, set of items (''things'') <math>T = \{t_1, t_2, ...\}</math> and set reviews <math>R=\{r_1, r_2, ...\}</math> where each <math>r_i=(u, t, \mathbf{w})</math> for some user, thing and words. | |
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| + | In order to | ||
== Dataset == | == Dataset == | ||
Revision as of 23:26, 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.
Assumptions
- The text in a user's review will reflect his personal tastes and preferences in restaurants. For instance, he might mention his favorite food, be particular about the service or tend to talk about the ambiance of the restaurant.
- By looking at all the reviews for a specific restaurant, we can infer the strengths/weaknesses of the restaurant. For instance, if many reviews talk about the excellent service, we can use this knowledge in our recommendation system.
Brief summary of method
For our problem, we assume that we have a set of users , set of items (things) and set reviews where each for some user, thing and words.
In order to
