Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Dynamic adaptation of user profiles in recommender systems

In a period of time in which the content available through the Internet increases exponentially and is more easily accessible every day, techniques for aiding the selection and extraction of important and personalised information are of vital importance. Recommender Systems (RS) appear as a tool to help the user in a decision making process by evaluating a set of objects or alternatives and aiding the user at choosing which one/s of them suits better his/her interests or preferences. Those preferences need to be accurate enough to produce adequate recommendations and should be updated if the user changes his/her likes or if they are incorrect or incomplete. In this work an adequate model for managing user preferences in a multi-attribute (numerical and categorical) environment is presented to aid at providing recommendations in those kinds of contexts. The evaluation process of the recommender system designed is supported by a new aggregation operator (Unbalanced LOWA) that enables the combination of the information that defines an alternative into a single value, which then is used to rank the whole set of alternatives. After the recommendation has been made, learning processes have been designed to evaluate the user interaction with the system to find out, in a dynamic and unsupervised way, if the user profile in which the recommendation process relies on needs to be updated with new preferences. The work detailed in this document also includes extensive evaluation and testing of all the elements that take part in the recommendation and learning processes

Preference elicitation techniques for group recommender systems

A key issue in group recommendation is how to combine the individual preferences of different users that form a group and elicit a profile that accurately reflects the tastes of all members in the group. Most Group Recommender Systems (GRSs) make use of some sort of method for aggregating the preference models of individual users to elicit a recommendation that is satisfactory for the whole group. In general, most GRSs offer good results, but each of them have only been tested in one application domain. This paper describes a domain-independent GRS that has been used in two different application domains. In order to create the group preference model, we select two techniques that are widely used in other GRSs and we compare them with two novel techniques. Our aim is to come up with a model that weighs the preferences of all the individuals to the same extent in such a way that no member in the group is particularly satisfied or dissatisfied with the final recommendations. © 2011 Elsevier Inc. All rights reserved.Partial support provided by Consolider Ingenio 2010 CSD2007-00022, Spanish Government Project MICINN TIN2008-6701-C03-01 and Valencian Government Project Prometeo 2008/051. FPU grant reference AP2009-1896 awarded to Sergio Pajares-Ferrando.García García, I.; Pajares Ferrando, S.; Sebastiá Tarín, L.; Onaindia De La Rivaherrera, E. (2012). Preference elicitation techniques for group recommender systems. Information Sciences. 189:155-175. https://doi.org/10.1016/j.ins.2011.11.037S15517518

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