Jointly Tackling User and Item Cold-start with Sequential Contentbased Recommendations

Sügavaid närvivõrke on edukalt kasutatud mitmetes soovitussüsteemides. Sessioonipõhised soovitussüsteemid on nende üks alaliik, milles modelleeritakse kasutajate ja toodete interaktsioone (klikke), selleks et genereerida kasutajale isikupäraseid soovitusi. Rekurrentsed närvivõrgud on viimastel aastatel muutunud eelistatuimaks lahenduseks mitmesuguste jadaandmete modelleerimisel, sh kasutajasessioonid, kuid olemasolevate lahenduste puuduseks on see, et need on jäigalt seotud tootekataloogi ja selles olevate toodetega. Uute toodete lisandumisel tuleb kogu mudel uuesti treenida. Üks võimalik lahendus sellele on toodete metainfo (pealkiri, kirjeldus, pilt) kasutuselevõtt, mis võimaldab tooteid identifitseerida nende sisu põhjal, mitte identifikaatori järgi. Samas teadaolevalt ei ole hetkel välja pakutud meetodit, mis lahendaks korraga nii uue toote kui ka uue kasutaja lisandumise probleemi sessioonipõhistes soovitussüsteemides.Töös pakutakse välja uudne arhitektuur sessioonipõhise soovitussüsteemi jaoks, mis kasutab toodete metainfol põhinevaid vektoresitusi. Mudelis kombineeritakse sessiooni jooksul külastatud toodete vektoresitused, selleks et ennustada järgmise toote vektoresitust. Selline lahendus võimaldab lisada tootekataloogi uusi tooteid ilma mudelit uuesti treenimata. Täiendavalt kasutatakse kasutaja sessiooni tema eelistuste modelleerimiseks, mis tähendab, et ennustatud järgmine toode sõltub kasutaja varasematest interaktsioonidest ja seega on tegemist isikupärase ennustusega. Eksperimendid viidi läbi Amazoni kasutajaarvustuste andmestiku peal ning tulemusi võrreldi GRU4Rec ja TransRec mudelitega. Pakutud lahendus saavutas võrreldavaid või paremaid tulemusi kui varasemad parimad mudelid ning võimaldab seejuures lihtsustada uute toodete või kasutajate lisamist.Deep learning has been successfully used in the context of recommender systems. Sequential recommender systems are a class of algorithms which model user-item interactions and their temporal relationship in order to generate relevant personalized recommendations. Recurrent neural networks have become the state-of-the-art approach for sequential modeling, but current approaches in the context of recommendation systems are tightly coupled with the catalog size and item identifiers. This imposes a problem when new items are to be incorporated into the list of recommendable products, the entire model needs to be retrained. Feature-rich item metadata has been successfully used to improve recommendation quality with both sequential and non-sequential recommenders. However, to the best of our knowledge, no attempt has been made to tackle the problem of newly encountered user and item in a sequence aware model with personalized recommendations. This work presents a novel architecture for context-aware item prediction based on embeddings. The model combines item embeddings within a sequence to dynamically predict an item embedding for the next interaction. This allows to incorporate new items without model retraining. Moreover, the proposed architecture implicitly models the user preferences from user-item interactions and is able to provide item embedding predictions that are personalized to the context of a user and therefore produce personalized recommendations. The results are compared with GRU4Rec and TransRec in the next interaction prediction task using the Amazon reviews public dataset, and our experiments show comparable or better results than state-of-the-art personalized models, with the added benefit of being able to add items or users without model retraining

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

A survey of context-aware recommendation schemes in event-based social networks

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In recent years, Event-based social network (EBSN) applications, such as Meetup and DoubanEvent, have received popularity and rapid growth. They provide convenient online platforms for users to create, publish, and organize social events, which will be held in physical places. Additionally, they not only support typical online social networking facilities (e.g., sharing comments and photos), but also promote face-to-face offline social interactions. To provide better service for users, Context-Aware Recommender Systems (CARS) in EBSNs have recently been singled out as a fascinating area of research. CARS in EBSNs provide the suitable recommendation to target users by incorporating the contextual factors into the recommendation process. This paper provides an overview on the development of CARS in EBSNs. We begin by illustrating the concept of the term context and the paradigms of conventional context-aware recommendation process. Subsequently, we introduce the formal definition of an EBSN, the characteristics of EBSNs, the challenges that are faced by CARS in EBSNs, and the implementation process of CARS in EBSNs. We also investigate which contextual factors are considered and how they are represented in the recommendation process. Next, we focus on the state-of-the-art computational techniques regarding CARS in EBSNs. We also overview the datasets and evaluation metrics for evaluation in this research area, and discuss the applications of context-aware recommendation in EBSNs. Finally, we point out research opportunities for the research community

Knowledge Extraction in Video Through the Interaction Analysis of Activities

Video is a massive amount of data that contains complex interactions between moving objects. The extraction of knowledge from this type of information creates a demand for video analytics systems that uncover statistical relationships between activities and learn the correspondence between content and labels. However, those are open research problems that have high complexity when multiple actors simultaneously perform activities, videos contain noise, and streaming scenarios are considered. The techniques introduced in this dissertation provide a basis for analyzing video. The primary contributions of this research consist of providing new algorithms for the efficient search of activities in video, scene understanding based on interactions between activities, and the predicting of labels for new scenes