際際滷shows by User: KonstantinBauman / http://www.slideshare.net/images/logo.gif 際際滷shows by User: KonstantinBauman / Wed, 15 Oct 2014 12:51:45 GMT 際際滷Share feed for 際際滷shows by User: KonstantinBauman Discovering Contextual Information from User Reviews /slideshow/discover-context-slides/40313036 discovercontextslides-141015125145-conversion-gate02
The paper presents a new method of discovering relevant contextual information from the user-generated reviews in order to provide better recommendations to the users when such reviews complement traditional ratings used in recommender systems. In particular, we classify all the user reviews into the context rich specific and context poor generic reviews and present a word-based and an LDA-based methods of extracting contextual information from the specific reviews. We also show empirically on the Yelp data that, collectively, these two methods extract almost all the relevant contextual information across three different applications and that they are complementary to each other: when one method misses certain contextual information, the other one extracts it from the reviews.]]>
The paper presents a new method of discovering relevant contextual information from the user-generated reviews in order to provide better recommendations to the users when such reviews complement traditional ratings used in recommender systems. In particular, we classify all the user reviews into the context rich specific and context poor generic reviews and present a word-based and an LDA-based methods of extracting contextual information from the specific reviews. We also show empirically on the Yelp data that, collectively, these two methods extract almost all the relevant contextual information across three different applications and that they are complementary to each other: when one method misses certain contextual information, the other one extracts it from the reviews.]]> Wed, 15 Oct 2014 12:51:45 GMT /slideshow/discover-context-slides/40313036 KonstantinBauman@slideshare.net(KonstantinBauman) Discovering Contextual Information from User Reviews KonstantinBauman The paper presents a new method of discovering relevant contextual information from the user-generated reviews in order to provide better recommendations to the users when such reviews complement traditional ratings used in recommender systems. In particular, we classify all the user reviews into the context rich specific and context poor generic reviews and present a word-based and an LDA-based methods of extracting contextual information from the specific reviews. We also show empirically on the Yelp data that, collectively, these two methods extract almost all the relevant contextual information across three different applications and that they are complementary to each other: when one method misses certain contextual information, the other one extracts it from the reviews. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/discovercontextslides-141015125145-conversion-gate02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The paper presents a new method of discovering relevant contextual information from the user-generated reviews in order to provide better recommendations to the users when such reviews complement traditional ratings used in recommender systems. In particular, we classify all the user reviews into the context rich specific and context poor generic reviews and present a word-based and an LDA-based methods of extracting contextual information from the specific reviews. We also show empirically on the Yelp data that, collectively, these two methods extract almost all the relevant contextual information across three different applications and that they are complementary to each other: when one method misses certain contextual information, the other one extracts it from the reviews.
Discovering Contextual Information from User Reviews from Konstantin Bauman
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