TF-IDF Based Contextual Post-Filtering Recommendation Algorithm in Complex Interactive Situations of Online to Offline: An Empirical Study

O2O accelerates the integration of online and offline, promotes the upgrading of industrial structure and consumption pattern, meanwhile brings the information overload problem. This paper develops a post-context filtering recommendation algorithm based on TF-IDF, which improves the existing algorithms. Combined with contextual association probability and contextual universal importance, a contextual preference prediction model was constructed to adjust the initial score of the traditional recommendation combined with item category preference to generate the final result. The example of the catering industry shows that the proposed algorithm is more effective than the improved algorithm

Preference-aware publish/subscribe delivery with diversity

In publish/subscribe systems, users describe their interests via subscriptions and are notified whenever new interesting events become available. Typically, in such systems, all sub-scriptions are considered equally important. However, due to the abundance of information, users may receive over-whelming amounts of events. In this paper, we propose us-ing a ranking mechanism based on user preferences, so that only top-ranked events are delivered to each user. Since many times top-ranked events are similar to each other, we also propose increasing the diversity of delivered events. Furthermore, we examine a number of different delivering policies for forwarding ranked events to users, namely a pe-riodic, a sliding-window and a history-based one. We have fully implemented our approach in SIENA, a popular pub-lish/subscribe middleware system, and report experimental results of its deployment. 1

Personnalisation d'analyses décisionnelles sur des données multidimensionnelles

This thesis investigates OLAP analysis personalization within multidimensional databases. OLAP analyse is modeled through a graph where nodes represent the analysis contexts and graph edges represent the user operations. The analysis context regroups the user query as well as result. It is well described by a specific tree structure that is independent on the visualization structures of data and query languages. We provided a model for user preferences on the multidimensional schema and values. Each preference is associated with a specific analysis context. Based on previous models, we proposed a generic framework that includes two personalization processes. First process, denoted query personalization, aims to enhancing user query with related preferences in order to produce a new one that generates a personalized result. Second personalization process is query recommendation that allows helping user throughout the OLAP data exploration phase. Our recommendation framework supports three recommendation scenarios, i.e., assisting user in query composition, suggesting the forthcoming query, and suggesting alternative queries. Recommendations are built progressively basing on user preferences. In order to implement our framework, we developed a prototype system that supports query personalization and query recommendation processes. We present experimental results showing the efficiency and the effectiveness of our approaches.Le travail présenté dans cette thèse aborde la problématique de la personnalisation des analyses OLAP au sein des bases de données multidimensionnelles. Une analyse OLAP est modélisée par un graphe dont les noeuds représentent les contextes d'analyse et les arcs traduisent les opérations de l'utilisateur. Le contexte d'analyse regroupe la requête et le résultat. Il est décrit par un arbre spécifique qui est indépendant des structures de visualisation des données et des langages de requête. Par ailleurs, nous proposons un modèle de préférences utilisateur exprimées sur le schéma multidimensionnel et sur les valeurs. Chaque préférence est associée à un contexte d'analyse particulier. En nous basant sur ces modèles, nous proposons un cadre générique comportant deux mécanismes de personnalisation. Le premier mécanisme est la personnalisation de requête. Il permet d'enrichir la requête utilisateur à l'aide des préférences correspondantes afin de générer un résultat qui satisfait au mieux aux besoins de l'usager. Le deuxième mécanisme de personnalisation est la recommandation de requêtes qui permet d'assister l'utilisateur tout au long de son exploration des données OLAP. Trois scénarios de recommandation sont définis : l'assistance à la formulation de requête, la proposition de la prochaine requête et la suggestion de requêtes alternatives. Ces recommandations sont construites progressivement à l'aide des préférences de l'utilisateur. Afin valider nos différentes contributions, nous avons développé un prototype qui intègre les mécanismes de personnalisation et de recommandation de requête proposés. Nous présentons les résultats d'expérimentations montrant la performance et l'efficacité de nos approches. Mots-clés: OLAP, analyse décisionnelle, personnalisation de requête, système de recommandation, préférence utilisateur, contexte d'analyse, appariement d'arbres de contexte

Unifying Qualitative and Quantitative Database Preferences to Enhance Query Personalization

Data drives all aspects of our society, from everyday life, to business, to medicine, and science. It is well-known that query personalization can be an effective technique in dealing with the data scalability challenge, primarily from the human point of view. In order to personalize their query results, users need to express their preferences in an effective manner. There are two types of preferences: qualitative and quantitative. Each preference type has advantages and disadvantages with respect to expressiveness. The most important disadvantage of the quantitative model is that it cannot support all types of preferences while the qualitative model can only create a partial order over the data, which makes it impossible to rank all the results. The hypothesis of this dissertation is that it is possible to overcome the disadvantages of each preference type by combining both of them, in a single model, using the notion of intensity. This dissertation presents such a hybrid model and a practical system that has the ability to convert the intensity values of qualitative preferences into intensity values of quantitative preferences, without losing the qualitative information. The intensity values allow to create a total order over the tuples in the database that match a user’s preferences as well as to significantly increase the coverage of preferences. Hence, the proposed model eliminates the disadvantages of the existing two types of preferences. This dissertation formalizes the hybrid model using a preference graph and proposes an algorithm for efficient preference combination, which is evaluated in an experimental prototype. The experiments show that: (1) intensity plays a crucial role in determining the order of selecting and applying the preferences, and simply ordering the preferences based on the intensity value is not necessarily sufficient; (2) the model can achieve three orders of magnitude increase in coverage compared to other alternatives; (3) the solution proposed outperforms other Top-k algorithms by being able to use both qualitative and quantitative preferences at the same time, and (4) the algorithm proposed is efficient in terms of time complexity, returning tuples ordered by the intensity value in a matter of seconds

Adding context to preferences

To handle the overwhelming amount of information currently available, personalization systems allow users to specify the information that interests them through preferences. Most often, users have different preferences depending on context. In this paper, we introduce a model for expressing such contextual preferences. Context is modeled as a set of multidimensional attributes. We formulate the context resolution problem as the problem of (a) identifying those preferences that qualify to encompass the context state of a query and (b) selecting the most appropriate among them. We also propose an algorithm for context resolution that uses a data structure, called the profile tree, that indexes preferences based on their associated context. Finally, we evaluate our approach from two perspectives: usability and performance. 1