Recommending Heterogeneous Entity based on Complex Task and Social Media Opinions to Improve Web Search Results

Abstract

With the times of the Multi-Screen users pursued the internet services closer to life The study found that users will use different devices to different services No matter which devices be used "Social" and "Search" are the most popular services Therefore it is an important issue to provide good services to users Conventional search engines usually consider a search query corresponding only to a simple task However the queries from users are driven by complex tasks The results are less than expected or repeated query It’s not a good user experience for the query that had latent goal We defined those searches as complex task search In this work we based on complex task to retrieval the task goal and help users to find the heterogeneous entity Our heterogeneous entity recommendation model (HERM) contains four stages First topic-event-based complex task model which is generated the subtask goal from complex task Second using search engine results and Wikipedia search result page to extract entity category for each subtask goal in entity category Third found the heterogeneous entities for each entity category and used SVM to identify the related between entity and entity category Finally we collected the opinions from social network resources (i e Facebook Google Plus) and based on opinions to rank entities For instance the complex task “travel to Beijing” may involve several subtask goals including “book flight” “reserve hotel” and “survey spot” We can find the entity category “airline” from “book flight” In next step we can find the entity (i e “Cathay Pacific Airways” “EVA Airways” “China Airlines”) from entity category and recommend to users We proposed that HERM used diverse web resources to opinions form social network to recommend heterogeneous entity for complex task

Date of Award	2015 Aug 11
Original language	English
Supervisor	Wen-Hsiang Lu (Supervisor)