Hybrid group recommendations for a travel service

Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations

Egy turisztikai ajánlórendszer modellje = A Model of a Touristic Recommender System

Jelen dolgozat célja egy olyan turisztikai ajánlórendszer elméleti alapjainak lefektetése, mely képes elegendően pontos és személyre szabott útvonalak tervezésére egy idegen városban, figyelembe véve a felhasználó igényeit és lehetőségeit. A dolgozat a fentiekben vázolt kutatási célokat követve hármas tagolású. A 2. fejezetben kísérletet teszünk a menetidőbecslés modellezésére, melyhez a szakirodalom alapos tanulmányozása során sem tapasztalt mennyiségű túranapló adatot használtunk fel. A túranaplók előkészítéséhez szükséges megbízható magasságadatok becslésére egy saját földfelszínt közelítő modellt építünk NASA adatokra alapozva, majd az így nyert magasság értékeket rendeljünk a meglévő szélességi és hosszúsági adatokhoz. Ezután Tobler korábbi munkája nyomán újrabecsüljük a sebességet az útszakasz meredekségével leíró függvényét, és ezt használjuk fel személyre szabott menetidő becslésre, illetve egy másik, átlagsebességeken alapuló eljárást is bemutatunk. A turisták igényeinek személyre szabott kielégítése érdekében képesnek kell lennünk a preferenciáik feltérképezésére, és ezt figyelembe véve megállapítani az egyes helyszínek meglátogatásához kapcsololódó relatív profitokat. Ennek érdekében a 3. fejezetben bemutatásra kerül az ajánlórendszerek széles szakirodalma, valamint néhány jelenleg használatos turisztikai ajánlórendszer, és ezeket figyelembe véve alkotjuk meg saját ajánlások adására alkalmas hibrid modellünket. Ez egyszerre épít a felhasználóktól kapott információkra, mellyel feltérképezhetjük preferenciáikat, illetve látványosságokra, mint termékek komplex struktúrájára, mely segít megérteni azok szerkezetét. Az empirikus vizsgálat során gyűjtött információk segítségével módunk nyílik a turisták tipusainak meghatározására, mely lehetőséget ad az ajánlások pontosítására. A 4. fejezetben egy, a turista igényeihez alkalmazkodni képes útvonaltervező algoritmust alkotunk meg. A modellezés során a várost egy N csúcsú irányítatlan gráffal jellemzünk, melynek minden csúcsa egy-egy potenciálisan meglátogatandó látványosságot jelképez, míg az élek a végpontjaikat összekötő legrövidebb utat hivatottak leírni. Az egyes csúcsokban begyűjthető profitokat a 3. fejezetben megalkotott ajánlórendszerünkből származtatjuk, így azok speciálisan az adott turista igényeihez igazodnak. A gráf élköltségei nem mások, mint a turista számára az adott két pont között vezető legrövidebb út menetideje. A feladat, hogy P nap alatt (napi T órában) olyan uta(ka)t járjon be a gráfon, mellyel a célfüggvénye értékét maximalizálja. Külön figyelmet szenteltünk egy olyan célfüggvény megalkotásának, mely merőben eltér az útvonaltervező algoritmusok gyakorlatától annak érdekében, hogy a felhasználók számára leginkább tetsző útvonalat legyünk képesek tervezni

User Preference Web Search -- Experiments with a System Connecting Web and User

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware supporting top-k answers and a user interface for querying and evaluation of search results. We integrate several tools (implementing our models and methods) into one framework connecting user with the web. The model represents user preferences with fuzzy sets and fuzzy logic, here understood as a scoring describing user satisfaction. This model can be acquired with explicit or implicit methods. Model-theoretic semantics is based on fuzzy description logic f-EL. User preference learning is based on our model of fuzzy inductive logic programming. Our system works both for English and Slovak resources. The primary application domain are job offers and job search, however we show extension to mutual investment funds search and a possibility of extension into other application domains. Our top-k search is optimized with own heuristics and repository with special indexes. Our model was experimentally implemented, the integration was tested and is web accessible. We focus on experiments with several users and measure their satisfaction according to correlation coefficients

Walk the line: Toward an efficient user model for recommendations in museums

International audienceContrary to many application domains, recommending items within a museum is not only a question of preferences. Of course, the visitors expect suggestions that are likely to interest or please them. However, additional factors should be taken into account. Recent works use the visiting styles or the shortest distance between items to adapt the list of recommendations. But, as far as we know, no model of the literature aims at inferring in real time a holistic user model which includes variables such as the crowd tolerance, the distance tolerance, the expected user control, the fatigue, the congestion points, etc. As a work-in-progress, we propose a new representation model which includes psychological, physical and social variables so as to increase user satisfaction and enjoyment. We show how we can infer these characteristics from the user observations (geolocalization over time, moving speed,. . .) and we discuss how we can use them jointly for a sequence recommendation purpose. This work is still in an early stage of development and remains more theoretical than experimental

Result Diversification in Search and Recommendation: A Survey

Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers. There has been growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey's main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the open research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.Comment: 20 page

Annual Report 2019-2020

LETTER FROM THE DEAN As I write this letter wrapping up the 2019-20 academic year, we remain in a global pandemic that has profoundly altered our lives. While many things have changed, some stayed the same: our CDM community worked hard, showed up for one another, and continued to advance their respective fields. A year that began like many others changed swiftly on March 11th when the University announced that spring classes would run remotely. By March 28th, the first day of spring quarter, we had moved 500 CDM courses online thanks to the diligent work of our faculty, staff, and instructional designers. But CDM’s work went beyond the (virtual) classroom. We mobilized our makerspaces to assist in the production of personal protective equipment for Illinois healthcare workers, participated in COVID-19 research initiatives, and were inspired by the innovative ways our student groups learned to network. You can read more about our response to the COVID-19 pandemic on pgs. 17-19. Throughout the year, our students were nationally recognized for their skills and creative work while our faculty were published dozens of times and screened their films at prestigious film festivals. We added a new undergraduate Industrial Design program, opened a second makerspace on the Lincoln Park Campus, and created new opportunities for Chicago youth. I am pleased to share with you the College of Computing and Digital Media’s (CDM) 2019-20 annual report, highlighting our collective accomplishments. David MillerDeanhttps://via.library.depaul.edu/cdmannual/1003/thumbnail.jp

Proceedings of the African Diaspora Conference on Sustainable Development

The authors urge the Western donor organizations to facilitate and support the take up of such more sustainable models

The Water Project: A short-term study abroad program design for Alfred University

The Water Project is a short term, faculty-led program, which will address the global water crisis by using the strengths of Alfred University (AU). The Water Project will include students from five different disciplines -Engineering, Art, Business, Language, and Anthropology- and have them work in collaboration with each other and the faculty of AU to create low-cost, ceramic water filters for a community in need of a clean water source in a rural area of Burkina Faso. The Water Project is not intended to be an imposing force on the community, but a welcomed collaborator that works to create a reciprocal partnership with the community. Although only one small step towards addressing the global water crisis, the Water Project is a responsible and bold first step for AU. The purpose of the Water Project is to form a reciprocal partnership between AU and a community in Gourma, Burkina Faso. This partnership will help to provide the basic human right of clean water. The program is designed to be a mutually beneficial arrangement between two vastly different populations: the community in Burkina Faso and AU. The program will work best if the community and AU collaborate and exchange ideas together to help create a mutual bond. The Water Project will bring light to the work being done in the area of host community evaluation and drive the field of international education towards furthering the research in this area. The Water Project will provide AU with an example of the best practices in the field of international education in the area of short term, faculty-led programming