Call Limit-Based Composite Service Selection

International audienceAPIs allow companies to export, via the Internet, their skills and know-how, or even to open up new markets and new media for sale. But to fully exploit the advantages of these services, customers, mainly developers, must be equipped with tools giving the possibility of being able to assemble different services together. Fortunately, the notion of service composition is quite advanced, and different tools exist to compose services. However, as APIs with similar functionality are expected to be provided by competing providers, the key challenge is to find the most relevant compositions. This issue has been addressed in the context of QoS-based composite service selection. The downside, in practice, customers choose services based on the number of call limits. In this paper, we propose an approach to select the most relevant compositions based on the notion of call limit. Specifically, we show how the call limits of the individual services can be aggregated to obtain the call limits of a given composition. Then, we introduce the notion of minimal budget skyline, which comprises the most interesting compositions that fit within the customer's budget. In addition, we develop two algorithms, based on effective pruning strategies, to efficiently compute the minimal budget skyline. Finally, we present a thorough experimental evaluation of our approach

A contextual and composite recommender system for the personalization of cultural sites visit

Notre travail concerne les systèmes d’aide à la visite de musée et l’accès au patrimoine culturel. L’objectif est de concevoir des systèmes de recommandation, implémentés sur dispositifs mobiles, pour améliorer l’expérience du visiteur, en lui recommandant les items les plus pertinents et en l’aidant à personnaliser son parcours. Nous considérons essentiellement deux terrains d’application : la visite de musées et le tourisme. Nous proposons une approche de recommandation hybride et sensible au contexte qui utilise trois méthodes différentes : démographique, sémantique et collaborative. Chaque méthode est adaptée à une étape spécifique de la visite de musée. L’approche démographique est tout d’abord utilisée afin de résoudre le problème du démarrage à froid. L’approche sémantique est ensuite activée pour recommander à l’utilisateur des œuvres sémantiquement proches de celles qu’il a appréciées. Enfin l’approche collaborative est utilisée pour recommander à l’utilisateur des œuvres que les utilisateurs qui lui sont similaires ont aimées. La prise en compte du contexte de l’utilisateur se fait à l’aide d’un post-filtrage contextuel, qui permet la génération d’un parcours personnalisé dépendant des œuvres qui ont été recommandées et qui prend en compte des informations contextuelles de l’utilisateur à savoir : l’environnement physique, la localisation ainsi que le temps de visite. Dans le domaine du tourisme, les points d’intérêt à recommander peuvent être de différents types (monument, parc, musée, etc.). La nature hétérogène de ces points d’intérêt nous a poussé à proposer un système de recommandation composite. Chaque recommandation est une liste de points d’intérêt, organisés sous forme de packages, pouvant constituer un parcours de l’utilisateur. L’objectif est alors de recommander les Top-k packages parmi ceux qui satisfont les contraintes de l’utilisateur (temps et coût de visite par exemple). Nous définissons une fonction de score qui évalue la qualité d’un package suivant trois critères : l’appréciation estimée de l’utilisateur, la popularité des points d’intérêt ainsi que la diversité du package et nous proposons un algorithme inspiré de la recherche composite pour construire la liste des packages recommandés. L’évaluation expérimentale du système que nous avons proposé, en utilisant un data-set réel extrait de Tripadvisor démontre sa qualité et sa capacité à améliorer à la fois la précision et la diversité des recommandations.Our work concerns systems that help users during museum visits and access to cultural heritage. Our goal is to design recommender systems, implemented in mobile devices to improve the experience of the visitor, by recommending him the most relevant items and helping him to personalize the tour he makes. We consider two mainly domains of application : museum visits and tourism. We propose a context-aware hybrid recommender system which uses three different methods : demographic, semantic and collaborative. Every method is adapted to a specific step of the museum tour. First, the demographic approach is used to solve the problem of the cold start. The semantic approach is then activated to recommend to the user artworks that are semantically related to those that the user appreciated. Finally, the collaborative approach is used to recommend to the user artworks that users with similar preferences have appreciated. We used a contextual post filtering to generate personalized museum routes depending on artworks which were recommended and contextual information of the user namely : the physical environment, the location as well as the duration of the visit. In the tourism field, the items to be recommended can be of various types (monuments, parks, museums, etc.). Because of the heterogeneous nature of these points of interest, we proposed a composite recommender system. Every recommendation is a list of points of interest that are organized in a package, where each package may constitute a tour for the user. The objective is to recommend the Top-k packages among those who satisfy the constraints of the user (time, cost, etc.). We define a scoring function which estimates the quality of a package according to three criteria : the estimated appreciation of the user, the popularity of points of interest as well as the diversity of packages. We propose an algorithm inspired by composite retrieval to build the list of recommended packages. The experimental evaluation of the system we proposed using a real world data set crawled from Tripadvisor demonstrates its quality and its ability to improve both the relevance and the diversity of recommendations

Un système de recommandation contextuel et composite pour la visite personnalisée de sites culturels

Our work concerns systems that help users during museum visits and access to cultural heritage. Our goal is to design recommender systems, implemented in mobile devices to improve the experience of the visitor, by recommending him the most relevant items and helping him to personalize the tour he makes. We consider two mainly domains of application : museum visits and tourism. We propose a context-aware hybrid recommender system which uses three different methods : demographic, semantic and collaborative. Every method is adapted to a specific step of the museum tour. First, the demographic approach is used to solve the problem of the cold start. The semantic approach is then activated to recommend to the user artworks that are semantically related to those that the user appreciated. Finally, the collaborative approach is used to recommend to the user artworks that users with similar preferences have appreciated. We used a contextual post filtering to generate personalized museum routes depending on artworks which were recommended and contextual information of the user namely : the physical environment, the location as well as the duration of the visit. In the tourism field, the items to be recommended can be of various types (monuments, parks, museums, etc.). Because of the heterogeneous nature of these points of interest, we proposed a composite recommender system. Every recommendation is a list of points of interest that are organized in a package, where each package may constitute a tour for the user. The objective is to recommend the Top-k packages among those who satisfy the constraints of the user (time, cost, etc.). We define a scoring function which estimates the quality of a package according to three criteria : the estimated appreciation of the user, the popularity of points of interest as well as the diversity of packages. We propose an algorithm inspired by composite retrieval to build the list of recommended packages. The experimental evaluation of the system we proposed using a real world data set crawled from Tripadvisor demonstrates its quality and its ability to improve both the relevance and the diversity of recommendations.Notre travail concerne les systèmes d’aide à la visite de musée et l’accès au patrimoine culturel. L’objectif est de concevoir des systèmes de recommandation, implémentés sur dispositifs mobiles, pour améliorer l’expérience du visiteur, en lui recommandant les items les plus pertinents et en l’aidant à personnaliser son parcours. Nous considérons essentiellement deux terrains d’application : la visite de musées et le tourisme. Nous proposons une approche de recommandation hybride et sensible au contexte qui utilise trois méthodes différentes : démographique, sémantique et collaborative. Chaque méthode est adaptée à une étape spécifique de la visite de musée. L’approche démographique est tout d’abord utilisée afin de résoudre le problème du démarrage à froid. L’approche sémantique est ensuite activée pour recommander à l’utilisateur des œuvres sémantiquement proches de celles qu’il a appréciées. Enfin l’approche collaborative est utilisée pour recommander à l’utilisateur des œuvres que les utilisateurs qui lui sont similaires ont aimées. La prise en compte du contexte de l’utilisateur se fait à l’aide d’un post-filtrage contextuel, qui permet la génération d’un parcours personnalisé dépendant des œuvres qui ont été recommandées et qui prend en compte des informations contextuelles de l’utilisateur à savoir : l’environnement physique, la localisation ainsi que le temps de visite. Dans le domaine du tourisme, les points d’intérêt à recommander peuvent être de différents types (monument, parc, musée, etc.). La nature hétérogène de ces points d’intérêt nous a poussé à proposer un système de recommandation composite. Chaque recommandation est une liste de points d’intérêt, organisés sous forme de packages, pouvant constituer un parcours de l’utilisateur. L’objectif est alors de recommander les Top-k packages parmi ceux qui satisfont les contraintes de l’utilisateur (temps et coût de visite par exemple). Nous définissons une fonction de score qui évalue la qualité d’un package suivant trois critères : l’appréciation estimée de l’utilisateur, la popularité des points d’intérêt ainsi que la diversité du package et nous proposons un algorithme inspiré de la recherche composite pour construire la liste des packages recommandés. L’évaluation expérimentale du système que nous avons proposé, en utilisant un data-set réel extrait de Tripadvisor démontre sa qualité et sa capacité à améliorer à la fois la précision et la diversité des recommandations

Call Limit-Based Composite Service Selection

International audienceAPIs allow companies to export, via the Internet, their skills and know-how, or even to open up new markets and new media for sale. But to fully exploit the advantages of these services, customers, mainly developers, must be equipped with tools giving the possibility of being able to assemble different services together. Fortunately, the notion of service composition is quite advanced, and different tools exist to compose services. However, as APIs with similar functionality are expected to be provided by competing providers, the key challenge is to find the most relevant compositions. This issue has been addressed in the context of QoS-based composite service selection. The downside, in practice, customers choose services based on the number of call limits. In this paper, we propose an approach to select the most relevant compositions based on the notion of call limit. Specifically, we show how the call limits of the individual services can be aggregated to obtain the call limits of a given composition. Then, we introduce the notion of minimal budget skyline, which comprises the most interesting compositions that fit within the customer's budget. In addition, we develop two algorithms, based on effective pruning strategies, to efficiently compute the minimal budget skyline. Finally, we present a thorough experimental evaluation of our approach

Top-k Cloud Service Plans Using Trust and QoS

International audienc

A Comparative Evaluation of Top-N Recommendation Algorithms: Case Study with Total Customers

International audienceIndustrial applications of recommendation systems aim at recommending top-N products that are the most appealing to their customers, often focusing on those products that customers are likely to purchase in the near future. In this experiments and analyses paper, we present an extensive experimental evaluation of various top-N collaborative filtering recommendation algorithms based on a real-world dataset of customer's purchase history provided by our business partners at TOTAL. Our study aims to compare representative collaborative filtering approaches in practice and study the ones yielding the highest recommendation accuracy, with respect to well-established evaluation measures. These experiments are part of the development of a promotional offers campaign for TOTAL customers owning a loyalty card. We show how different settings for training and applying the selected algorithms influence their absolute and relative performances. The results are valuable to our TOTAL partners as they constitute the first large-scale analysis of recommendation algorithms in the context of their datasets. In particular, the study of the impact of recency in the training set and the role of customer activity and of context in recommendation shed light on a finer design of promotional product campaigns

How Useful is Meta-Recommendation? An Empirical Investigation

International audienceDespite the proliferation of recommendation algorithms, the question of which recommender works best for which user-item instance remains widely open. In this paper, we develop a meta-learning approach that chooses among several recommendation algorithms, which one is best suited for predicting the preference of a user for an item. We propose an empirical investigation of the meta-learner when applied to implicit and explicit datasets. The meta-learner is trained using four classifiers/regressors: logistic regression, decision trees, stochastic gradient descent, and gradient boosting. We run extensive experiments on four real datasets: RETAIL, a proprietary implicit dataset provided by our industrial partner, TAFENG, a publicly available grocery shopping dataset and two publicly available AMAZON datasets with explicit preferences. Results show that using a meta-learner yields higher accuracy than single recommendation algorithms for explicit datasets when compared to state-of-the-art ensemble-learned models and factorization machines. This work is an ongoing collaboration with the marketing department of a major industrial partner to test promotional offers for different customer segments

Significance and Coverage in Group Testing on the Social Web

International audienc

Multi-Objective Recommendations and Promotions at TOTAL

International audienceIn this paper, we revisit the semantics of recommendations and promotional offers using multi-objective optimization principles. We investigate two formulations of product recommendation that go beyond traditional settings by optimizing simultaneously two conflicting objectives: Budget-Reco optimizes two customer-centric goals, namely utility and budget, and Business-Reco optimizes utility, a customer-centric goal, and profit margin, a business-oriented goal. To capture those objectives, we formulate knapsack problems and propose adaptations of exact and approximate algorithms. We also propose Group-Promo, the problem of generating product promotions that we model as a group discovery problem with multiple objectives and develop a Pareto-based solution. Our experiments on our TOTAL datasets demonstrate the importance of multi-objective optimization in the retail context, as well as the usefulness of our solutions when compared to their exact baselines. The results are valuable to TOTAL's marketing department that has been improving hand-crafted strategies by launching several promotional campaigns using our algorithms

A Bi-Objective Approach for Product Recommendations

International audienceWe propose a bi-objective formulation for product recommendations. Our formulation goes beyond traditional recommendations by capturing two conflicting objectives: utility that serves customers' interests, and profit margin, a business-oriented goal. To satisfy the needs of our business partners, we formulate a new problem, namely generating a result containing all sets of k products such that there does not exist any other set of k products that dominates the returned sets, i.e., whose cumulative values for each objective is higher than a set of k products in the result. We study properties of k-Pareto sets that enable us to reduce the number of candidates, as well as the number of dominance tests between candidate sets. We develop a dynamic programming algorithm that leverages those properties to prune the space of solutions. We generalize traditional measures of recommendation accuracy to be applicable to sets of k products. Our experiments on a large set of real customer transactions validate the need for a bi-objective optimization to reconcile customer and business interests, and the scalability of our solution