Resolving Ambiguity Using Word Embeddings for Personalized Information Retrieval in Folksonomy Systems

Document Type : Original Article


1 Department of Computer Engineering, University of Science and Culture, Tehran, Iran

2 Scientific Information Database(SID), Academic Center for Education, Culture and Research ACECR


The diversity and high volume of available information on the web make data retrieval a serious challenge in this environment. On the other hand, obtaining user satisfaction is difficult, which is one of the main challenges of data retrieval systems. Depending on their information about interests and needs for the same keyword, different people expect different responses from Information Retrieval (IR) systems. Achieving this goal requires an effective method to retrieve information. Personalized Information Retrieval (PIR) is an effective method to achieve this goal which is considered by researchers today.  Folksonomy is the process that allows users to tag in a specific domain of information in a social environment (tags are accessible to other users). Folksonomy systems are made collaborative tagging systems. Due to the large volume and variety of tags produced, resolving ambiguity is a severe challenge in these systems. In recent years, word embedding methods have been considered by researchers as a successful method to fix the ambiguity of texts.
This study proposes a model which, in addition to using word embedding methods to remove tag ambiguity, provides search results in a personalized approach by fixing ambiguity and sentiment analysis combination tailored to users' interests. In this research, different models of word embeddings were applied. The experiments' results show that after applying the fixing ambiguity, the mean accuracy criterion improved by 1.93% and the mean MRR (Mean Reciprocal Rank)  by 0.38%.


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Ghazale Etemadikhu is a graduate student in master of software engineering at the University of Science and Culture, Tehran, Iran. Her research interest is information retrieval.

Fatemeh Azimzadeh received a Ph.D. degree in Information Technology from University Putra Malaysia in 2012. Currently, she is an assistant professor in ACECR, Tehran, Iran. She is also the director of SID (Scientific Information Database) in Iran. Her research interests include information retrieval and information quality.

Abdalsamad Keramatfar is a Ph.D. student in information technology at the University of Qom and a data scientist at SID. He currently works in natural language processing and machine learning and specifically on multi-thread modeling of context for social media sentiment analysis. His research interests are artificial intelligence and natural language