LDA-BASED PERSONALIZED DOCUMENT RECOMMENDATION

Abstrac

SENTIGRADE: A SENTIMENT BASED USER PROFILING STRATEGY FOR PERSONALISATION

Nowadays, the availability of folksonomy data is increased to make importance for user profiling approaches to provide results of the retrieval data or personalized recommendation. The approach is used for detecting the preferences for users and can be able to understand the interest of the user in a better way. In this approach, the incorporation of information with numerous data which depends upon sentiment is implemented using a framework SentiGrade by User Profiles (UP) and Resource Profiles (RP) for user Personalized Search (PS). From the folksonomy data, the discovery of User Preference (UsP) is presented by a rigorous probabilistic framework and relevance method are proposed for obtaining Sentiment-Based Personalized (SBP) ranking. According to the evaluation of the approach, the proposed SBP search is compared with the existing method and uses the two datasets namely, Movielens and FMRS databases. The experimental outcome of the research proved the effectiveness of the framework and works well when compared to the existing method. Through user study, the evaluation of approaches and developed systems are made which shows that considering information such as relevance and probabilistic data in Web Personalization (WP) systems can able to offer better recommendations and provide much effective personalization services to users

A Random Walk Model for Item Recommendation in Social Tagging Systems

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users\u27 preferences on an item similarity graph and spreading items\u27 influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation

Semantic image retrieval using relevance feedback and transaction logs

Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible, and efficient means to retrieve images matching a user’s query are needed. Content-based Image Retrieval (CBIR) systems automatically extract image contents based on image features, i.e. color, texture, and shape. Relevance feedback methods are applied to CBIR to integrate users’ perceptions and reduce the gap between high-level image semantics and low-level image features. The precision of a CBIR system in retrieving semantically rich (complex) images is improved in this dissertation work by making advancements in three areas of a CBIR system: input, process, and output. The input of the system includes a mechanism that provides the user with required tools to build and modify her query through feedbacks. Users behavioral in CBIR environments are studied, and a new feedback methodology is presented to efficiently capture users’ image perceptions. The process element includes image learning and retrieval algorithms. A Long-term image retrieval algorithm (LTL), which learns image semantics from prior search results available in the system’s transaction history, is developed using Factor Analysis. Another algorithm, a short-term learner (STL) that captures user’s image perceptions based on image features and user’s feedbacks in the on-going transaction, is developed based on Linear Discriminant Analysis. Then, a mechanism is introduced to integrate these two algorithms to one retrieval procedure. Finally, a retrieval strategy that includes learning and searching phases is defined for arranging images in the output of the system. The developed relevance feedback methodology proved to reduce the effect of human subjectivity in providing feedbacks for complex images. Retrieval algorithms were applied to images with different degrees of complexity. LTL is efficient in extracting the semantics of complex images that have a history in the system. STL is suitable for query and images that can be effectively represented by their image features. Therefore, the performance of the system in retrieving images with visual and conceptual complexities was improved when both algorithms were applied simultaneously. Finally, the strategy of retrieval phases demonstrated promising results when the query complexity increases

Recommender systems in industrial contexts

This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: this system, Reperio, is a hybrid recommender engine using KNN methods. We study the performance of the KNN methods, including the impact of similarity functions used. Then we study the performance of the KNN method in critical uses cases in cold start situation. - A methodology for analyzing the performance of recommender systems in industrial context: this methodology assesses the added value of algorithmic strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201

SUMMARIZATION AND VISUALIZATION OF DIGITAL CONVERSATIONS

Digital conversations are all around us: recorded meetings, television debates, instant messaging, blogs, and discussion forums. With this work, we present some solutions for the condensation and distillation of content from digital conversation based on advanced language technology. At the core of this technology we have argumentative analysis, which allow us to produce high-quality text summaries and intuitive graphical visualizations of conversational content enabling easier and faster access to digital conversations

What can we recommend to game players? - Implementing a system of analyzing game reviews

With the rapid development of game industry, games take an increasing important role of entertainment in our daily life. With more and more new games coming to the market, it is difficult for players to choose suitable games. This thesis proposed a way of analyzing game user reviews to help players selecting games. A large amount of game reviews is generated by game players in text format every day, evaluating thousands of reviews one by one becomes impossible. Topic mining techniques makes it possible to extract the topics(aspects) from a large amount of textual data and summarize the reviews in a general level. A lot of research has put efforts on developing and optimizing topic mining algorithms, including developing systems or applications based on these algorithms to analyze user review data from different sources. Theses source includes social media platforms such as Twitter, mobile application stores such as Google Play, movies websites such as Netflix, etc. However, there is a lack of an application especially focus on game reviews. The aim of this thesis is to develop a system which covers all the necessary steps of topic mining of game reviews, including review data collecting, storage, processing and visualization. Topics generated by the system are supposed to reveal the content of the game and user’s game experience. Testing and evaluation of the system are conducted after implementation. After a series of experiments, it is validated that the system runs smoothly in real-world situations and produces suitable topics that can help users to understand and select games

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user's request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks