Rocchio Algorithm to Enhance Semantically Collaborative Filtering

International audienceRecommender system provides relevant items to users from huge catalogue. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Collaborative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for recommendation. Hybrid recommendation system combines the two techniques. In this paper, we present another hybridization approach: User Semantic Collaborative Filtering. The aim of our approach is to predict users preferences for items based on their inferred preferences for semantic information of items. In this aim, we design a new user semantic model to describe the user preferences by using Rocchio algorithm. Due to the high dimension of item content, we apply a latent semantic analysis to reduce the dimension of data. User semantic model is then used in a user-based collaborative filtering to compute prediction ratings and to provide recommendations. Applying our approach to real data set, the MoviesLens 1M data set, significant improvement can be noticed compared to usage only approach, content based only approach

Studying, developing, and experimenting contextual advertising systems

The World Wide Web has grown so fast in the last decade and it is today a vital daily part of people. The Internet is used for many purposes by an ever growing number of users, mostly for daily activities, tasks, and services. To face the needs of users, an efficient and effective access to information is required. To deal with this task, the adoption of Information Retrieval and Information Filtering techniques is continuously growing. Information Re-trieval (IR) is the field concerned with searching for documents, information within documents, and metadata about documents, as well as searching for structured storage, relational databases, and the World Wide Web. Infor- mation Filtering deals with the problem of selecting relevant information for a given user, according to her/his preferences and interest. Nowadays, Web advertising is one of the major sources of income for a large number of websites. Its main goal is to suggest products and services to the still ever growing population of Internet users. Web advertising is aimed at suggesting products and services to the users. A significant part of Web ad-vertising consists of textual ads, the ubiquitous short text messages usually marked as sponsored links. There are two primary channels for distributing ads: Sponsored Search (or Paid Search Advertising) and Contextual Ad-vertising (or Content Match). Sponsored Search advertising is the task of displaying ads on the page returned from a Web search engine following a query. Contextual Advertising (CA) displays ads within the content of a generic, third party, webpage. In this thesis I study, develop, and evaluated novel solutions in the field of Contextual Advertising. In particular, I studied and developed novel text summarization techniques, I adopted a novel semantic approach, I studied and adopted collaborative approaches, I started a conjunct study of Contex-tual Advertising and Geo-Localization, and I study the task of advertising in the field of Multi-Modal Aggregation. The thesis is organized as follows. In Chapter 1, we briefly describe the main aspects of Information Retrieval. Following, the Chapter 2 shows the problem of Contextual Advertising and describes the main contributes of the literature. Chapter 3 sketches a typical adopted approach and the eval-uation metrics of a Contextual Advertising system. Chapter 4 is related to the syntactic aspects, and its focus is on text summarization. In Chapter 5 the semantic aspects are taken into account, and a novel approach based on ConceptNet is proposed. Chapter 6 proposes a novel view of CA by the adoption of a collaborative filtering approach. Chapter 7 shows a prelim-inary study of Geo Location, performed in collaboration with the Yahoo! Research center in Barcelona. The target is to study several techniques of suggesting localized advertising in the field of mobile applications and search engines. In Chapter 8 is shown a joint work with the RAI Centre for Research and Technological Innovation. The main goal is to study and propose a system of advertising for Multimodal Aggregation data. Chapter 9 ends this work with conclusions and future directions

Personalization in cultural heritage: the road travelled and the one ahead

Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

Personalizing type-based facet ranking using BERT embeddings

In Faceted Search Systems (FSS), users navigate the information space through facets, which are attributes or meta-data that describe the underlying content of the collection. Type-based facets (aka t-facets) help explore the categories associated with the searched objects in structured information space. This work investigates how personalizing t-facet ranking can minimize user effort to reach the intended search target. We propose a lightweight personalisation method based on Vector Space Model (VSM) for ranking the t-facet hierarchy in two steps. The first step scores each individual leaf-node t-facet by computing the similarity between the t-facet BERT embedding and the user profile vector. In this model, the user's profile is expressed in a category space through vectors that capture the users' past preferences. In the second step, this score is used to re-order and select the sub-tree to present to the user. The final ranked tree reflects the t-facet relevance both to the query and the user profile. Through the use of embeddings, the proposed method effectively handles unseen facets without adding extra processing to the FSS. The effectiveness of the proposed approach is measured by the user effort required to retrieve the sought item when using the ranked facets. The approach outperformed existing personalization baselines

An Intelligent Technique for Extracting Subjects from User Profile Using ODP Ontology-Driven Reasoning

Abstract: Nowadays, the amount of available information, especially on the Web, is increasing. In this field, the role of user modeling and personalized information access is obviously vital. The traditional techniques like BOW (Bags of words) limit recommendations to the words which have been stored in the profile. In other words, the news items, which semantically relate to the users interests, can't be recognized and recommended to the users. Besides, BOW technique suffers from the curse of dimensionality, thus computational burden reduction is an essential task to efficiently handle a large number of terms in practical applications. This study focuses on the problem of choosing a representation of documents that can be suitable to induce concept-based user profiles as well as to support a content-based retrieval process. In this study, a new approach has been proposed to construct a ranked semantic user profile through extracting the related subjects. The new items can be recommended through collecting information from the user's selections, based on existing domain ontology ODP. The efficiency of the proposed technique has been shown by embedding it into an intelligent aggregator, RSS (RSS is acronym of " Really Simple Syndication) feed reader, which has been trained and evaluated by different and heterogeneous users. The results in experimental session show that the incoming news item which semantically relates to the profile gets highly recommended to the user despite its excluding of common words in the profile

Conceptual, Impact-Based Publications Recommendations

CiteSeerx is a digital library for scientific publications by computer science researchers. It also functions as a search engine with several features including autonomous citation indexing, automatic metadata extraction, full-text indexing and reference linking. Users are able to retrieve relevant documents from the CiteSeerx database directly using search queries and will further benefit if the system suggests document recommendations to the user based on their preferences and search history. Therefore, recommender systems were initially developed and continue to evolve to recommend more relevant documents to the CiteSeerx users. In this thesis, we introduce the Conceptual, Impact-Based Recommender (CIBR), a hybrid recommender system, derived from the previously implemented conceptual recommender system in CiteSeerx. The Conceptual recommender system utilized the user\u27s top weighted concepts to recommend relevant documents to the users. Our hybrid recommender system, CIBR, considers the impact factor in addition to the top weighted concepts for generating recommendations for the user. The impact factor of a document is determined by using the author\u27s h-index of the publication. A survey was conducted to evaluate the efficiency of our hybrid system and this study shows that the CIBR system generates more relevant documents as compared to those recommended by the conceptual recommender system

An explainable recommender system based on semantically-aware matrix factorization.

Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the ratings for unseen items with high accuracy. Matrix factorization is an accurate collaborative filtering method used to predict user preferences. However, it is a black box system that recommends items to users without being able to explain why. This is due to the type of information these systems use to build models. Although rich in information, user ratings do not adequately satisfy the need for explanation in certain domains. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of big data and machine learning methods to a mass audience without a compromise in trust. Explanations can take a variety of formats, depending on the recommendation domain and the machine learning model used to make predictions. Semantic Web (SW) technologies have been exploited increasingly in recommender systems in recent years. The SW consists of knowledge graphs (KGs) providing valuable information that can help improve the performance of recommender systems. Yet KGs, have not been used to explain recommendations in black box systems. In this dissertation, we exploit the power of the SW to build new explainable recommender systems. We use the SW\u27s rich expressive power of linked data, along with structured information search and understanding tools to explain predictions. More specifically, we take advantage of semantic data to learn a semantically aware latent space of users and items in the matrix factorization model-learning process to build richer, explainable recommendation models. Our off-line and on-line evaluation experiments show that our approach achieves accurate prediction with the additional ability to explain recommendations, in comparison to baseline approaches. By fostering explainability, we hope that our work contributes to more transparent, ethical machine learning without sacrificing accuracy