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Social networks form an important platform for information sharing and interaction among users. The content from social networks can be used to generate recommendations for users in order to help them to choose what they desire. There exist a lot of recommendation methods currently. In this paper, we propose a keyword
Recently, keyword search on XML data has received much attention. Existing XML keyword search algorithms are all based on Dewey labeling, and this method in the calculation of the common ancestor would suffer from the CAR (common-ancestor-repetition) problem. In this paper, we propose a novel index based on interval
Keyword-based search exploits the exact match between the index terms of a query and documents. Thus, some documents, although they are relevant to the given query, may not be returned to users unless the documents include the index terms of the query. Some search engines use the authority of documents, which is
traditional keyword based search, and provides recommendation that fits the user's personal preferences better. We demonstrate our method by applying it to product review recommendation based on user preferred composition style.
The existing search engines are always lack of the consideration of personalization and display the same search results for different users despite their differences in interesting and purpose. So through analyzing the dynamic search behavior of users, the paper introduces a new method of using a keyword query graph
system. The retrieval system is able to retrieve videos based on emotional keyword query as well as arousal and valence query. The user's personal profile (gender, age, cultural background) was employed to improve the collaborative filtering in retrieval.
Methods of retrieving images that incorporate human- generated metadata, such as keyword annotation and collaborative filtering, are less vulnerable to the semantic gap than content-based image retrieval. However, generating such metadata is time-consuming, expensive, and difficult to evaluate. This paper discusses an
mismatch problem and match irrelevance problem and fail to generate highly related results. To overcome these problems, we propose a novel approach to recommend articles to the researchers. In our approach we integrate three types of similarity measures: keyword similarity, journal similarity, and author similarity to measure
Web service recommendation has become a critical problem as services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques such as keyword search and semantic matching while others are based on Quality of Service (QoS) prediction. However, services and their mashups
the historical browsers' data for search keywords and provides users with most relevant web pages. All the users click-through activity such as number of times he visited, duration he spent, his mouse movements and several other variables are stored in database. The proposed system uses this database and process to rank
Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different types of recommendations: They can recommend 1) tags to users, based on what tags other users
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.