Restaurant search and recommendation system is a very popular service in many countries. In those systems, most of the restaurant information such as restaurant name, address, phone number, and introduction are collected manually. In this paper, we propose a restaurant information extraction method which can automatically extract restaurant information from online reviews of restaurants in blogs. In addition, by calculating TFIDFs of words in blog posts, the hot keywords can be discovered and ranked. For restaurant search, users are allowed to search by keywords, areas, and/or extracted hot keywords. The experimental results show that the proposed method can achieve over 90 % average accuracy of hot keyword extraction and about 95 % mean average precision for restaurant search. In user study, the fact that the proposed system is more useful than Google search in restaurant search is presented.
|Title of host publication||18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)|
|Number of pages||12|
|State||Published - 2014|
|Name||Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|
- Information retrieval; Opinion mining; TFIDF; Food and restaurants; Restaurant search