A Personalized Facet-Weight Based Ranking Method for Service Component Retrieval

With the recent advanced computing, networking technologies and embedded systems, the computing paradigm has switched from mainframe and desktop computing to ubiquitous computing, one of whose visions is to provide intelligent, personalized and comprehensive services to users. As a new paradigm, Active Services is proposed to generate such services by retrieving, adapting, and composing of existing service components to satisfy user requirements. As the popularity of this paradigm and hence the number of service components increases, how to efficiently retrieve components to maximally meet user requirements has become a fundamental and significant problem. However, traditional facet-based retrieval methods only simply list out all the results without any kind of ranking and do not lay any emphasis on the differences of importance on each facet value in user requirements, which makes it hard for user to quickly select suitable components from the resulting list. To solve the problems, this paper proposes a novel personalized facet-weight based ranking method for service component retrieval, which assigns a weight for each facet to distinguish the importance of the facets, and constructs a personalized model to automatically calculate facet-weights for users according to their histo -rical retrieval records of the facet values and the weight setting. We optimize the parameters of the personalized model, evaluate the performance of the proposed retrieval method, and compare with the traditional facet-based matching methods. The experimental results show promising results in terms of retrieval accuracy and execution time

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

Colombus: providing personalized recommendations for drifting user interests

The query formulationg process if often a problematic activity due to the cognitive load that it imposes to users. This issue is further amplified by the uncertainty of searchers with regards to their searching needs and their lack of training on effective searching techniques. Also, given the tremendous growth of the world wide web, the amount of imformation users find during their daily search episodes is often overwhelming. Unfortunatelly, web search engines do not follow the trends and advancements in this area, while real personalization features have yet to appear. As a result, keeping up-to-date with recent information about our personal interests is a time-consuming task. Also, often these information requirements change by sliding into new topics. In this case, the rate of change can be sudden and abrupt, or more gradual. Taking into account all these aspects, we believe that an information assistant, a profile-aware tool capable of adapting to users’ evolving needs and aiding them to keep track of their personal data, can greatly help them in this endeavor. Information gathering from a combination of explicit and implicit feedback could allow such systems to detect their search requirements and present additional information, with the least possible effort from them. In this paper, we describe the design, development and evaluation of Colombus, a system aiming to meet individual needs of the searchers. The system’s goal is to pro-actively fetch and present relevant, high quality documents on regular basis. Based entirely on implicit feedback gathering, our system concentrates on detecting drifts in user interests and accomodate them effectively in their profiles with no additional interaction from their side. Current methodologies in information retrieval do not support the evaluation of such systems and techniques. Lab-based experiments can be carried out in large batches but their accuracy often questione. On the other hand, user studies are much more accurate, but setting up a user base for large-scale experiments is often not feasible. We have designed a hybrid evaluation methodology that combines large sets of lab experiments based on searcher simulations together with user experiments, where fifteen searchers used the system regularly for 15 days. At the first stage, the simulation experiments were aiming attuning Colombus, while the various component evaluation and results gathering was carried out at the second stage, throughout the user study. A baseline system was also employed in order to make a direct comparison of Colombus against a current web search engine. The evaluation results illustrate that the Personalized Information Assistant is effective in capturing and satisfying users’ evolving information needs and providing additional information on their behalf

A personalized hybrid music recommender based on empirical estimation of user-timbre preference

Automatic recommendation system as a subject of machine learning has been undergoing a rapid development in the recent decade along with the trend of big data. Particularly, music recommendation is a highlighted topic because of its commercial value coming from the large music industry. Popular online music recommendation services, including Spotify, Pandora and Last.FM use similarity-based approaches to generate recommendations. In this thesis work, I propose a personalized music recommendation approach that is based on probability estimation without any similarity calculation involved. In my system, each user gets a score for every piece of music. The score is obtained by combining two estimated probabilities of an acceptance. One estimated probability is based on the user’s preferences on timbres. Another estimated probability is the empirical acceptance rate of a music piece. The weighted arithmetic mean is evaluated to be the best performing combination function. An online demonstration of my system is available at www.shuyang.eu/plg/. Demonstrating recommendation results show that the system works effectively. Through the algorithm analysis on my system, we can see that my system has good reactivity and scalability without suffering cold start problem. The accuracy of my recommendation approach is evaluated with Million Song Dataset. My system achieves a pairwise ranking accuracy of 0.592, which outperforms random ranking (0.5) and ranking by popularity (0.557). Unfortunately, I have not found any other music recommendation method evaluated with ranking accuracy yet. As a comparison, Page Rank algorithm (for web page ranking) has a pairwise ranking accuracy of 0.567

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Music recommender systems. Proof of concept

Data overload is a well-known problem due to the availability of big on-line distributed databases. While providing a wealth of information the difficulties to find the sought data and the necessary time spent in the search call for technological solutions. Classical search engines alleviate this problem and at the same time have transformed the way people access to the information they are interested in. On the other hand, Internet also has changed the music consuming habits around the world. It is possible to find almost every recorded song or music piece. Over the last years music streaming platforms like Spotify, Apple Music or Amazon Music have contributed to a substantial change of users’ listening habits and the way music is commercialized and distributed. On-demand music platforms offer their users a huge catalogue so they can do a quick search and listen what they want or build up their personal library. In this context Music Recommender Systems may help users to discover music that match their tastes. Therefore music recommender systems are a powerful tool to make the most of an immense catalogue, impossible to be fully known by a human. This project aims at testing different music recommendation approaches applied to the particular case of users playlists. Several recommender alternatives were designed and evaluated: collaborative filtering systems, content-based systems and hybrid recommender systems that combine both techniques. Two systems are proposed. One system is content-based and uses correlation between tracks characterized by high-level descriptors and the other is an hybrid recommender that first apply a collaborative method to filter the database and then computes the final recommendation using Gaussian Mixture Models. Recommendations were evaluated using objective metrics and human evaluations, obtaining positive results.Ingeniería de Sistemas Audiovisuale