376 research outputs found
Cross-domain Recommendations based on semantically-enhanced User Web Behavior
Information seeking in the Web can be facilitated by recommender systems that guide the users in a personalized manner to relevant resources in the large space of the possible options in the Web. This work investigates how to model people\u27s Web behavior at multiple sites and learn to predict future preferences, in order to generate relevant cross-domain recommendations. This thesis contributes with novel techniques for building cross-domain recommender systems in an open Web setting
Recommender systems in industrial contexts
This thesis consists of four parts: - An analysis of the core functions and
the prerequisites for recommender systems in an industrial context: we identify
four core functions for recommendation systems: Help do Decide, Help to
Compare, Help to Explore, Help to Discover. The implementation of these
functions has implications for the choices at the heart of algorithmic
recommender systems. - A state of the art, which deals with the main techniques
used in automated recommendation system: the two most commonly used algorithmic
methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization
methods are detailed. The state of the art presents also purely content-based
methods, hybridization techniques, and the classical performance metrics used
to evaluate the recommender systems. This state of the art then gives an
overview of several systems, both from academia and industry (Amazon, Google
...). - An analysis of the performances and implications of a recommendation
system developed during this thesis: this system, Reperio, is a hybrid
recommender engine using KNN methods. We study the performance of the KNN
methods, including the impact of similarity functions used. Then we study the
performance of the KNN method in critical uses cases in cold start situation. -
A methodology for analyzing the performance of recommender systems in
industrial context: this methodology assesses the added value of algorithmic
strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201
A Taxonomy of Sequential Patterns Based Recommendation Systems
With remarkable expansion of information through the internet, users prefer to receive the exact information they need through some suggestions to save their time and money. Thus, recommendation systems have become the heart of business strategies of E-commerce as they can increase sales and revenue as well as customer loyalty. Recommendation systems techniques provide suggestions for items/products to be purchased, rented or used by a user. The most common type of recommendation system technique is Collaborative Filtering (CF), which takes user’s interest in an item (explicit rating) as input in a matrix known as the user-item rating matrix, and produces an output for unknown ratings of users for items from which top N recommended items for target users are defined. E-commerce recommendation systems usually deal with massive customer sequential databases such as historical purchase or click sequences. The time stamp of a click or purchase event is an important attribute of each dataset as the time interval between item purchases may be useful to learn the next items for purchase by users. Sequential Pattern Mining mines frequent or high utility sequential patterns from a sequential database. Recommendation systems accuracy will be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user-item rating matrix input. Thus, integrating collaborative filtering (CF) and sequential pattern mining (SPM) of historical clicks and purchase data can improve recommendation accuracy, diversity and quality and this survey focuses on review of existing recommendation systems that are sequential pattern based exposing their methodologies, achievements, limitations, and potentials for solving more problems in this domain. This thesis provides a comprehensive and comparative study of the existing Sequential Pattern-based E-commerce recommendation systems (SP-based E-commerce RS) such as ChoRec05, ChenRec09, HuangRec09, LiuRec09, ChoiRec12, Hybrid Model RecSys16, Product RecSys16, SainiRec17, HPCRec18 and HSPCRec19. Thesis shows that integrating sequential patterns mining (SPM) of historical purchase and/or click sequences into user-item matrix for collaborative filtering (CF) (i) Improved recommendation accuracy (ii) Reduced limiting user-item rating data Sparsity (iii) Increased Novelty Rate of the recommendations and (iv) Improved Scalability of the recommendation system. Thus, the importance of sequential patterns of customer behavior in improving the quality of recommendation systems for the application domain of E-commerce is accentuated through this survey by having a comparative performance analysis of the surveyed systems
Advances in session-based and session-aware recommendation
As of today, personalized item suggestions provided by an automated recommender system have become a crucial part of many online services, e.g., online shops or media streaming applications, and extensive evidence exists that such systems increase both the user experience as well as the revenue of the providers. In academia, the recommendation problem is often framed as finding suitable items that a user is not yet aware of based on his long-term preference profile. In the real world, however, this problem formulation has a number of problems.
Long-term profiles, e.g., are not available for new or anonymous users and recommendations can then only be based on the few most recent interactions in an ongoing usage session. Various approaches to this highly relevant setting of session-based recommendation that recently emerged in the research community were proposed over the recent years. However, in terms of the evaluation procedure, no common standard has been established so far. In this thesis, the author, therefore, proposes a publicly available framework for reproducible research and, furthermore, fairly compares many approaches, of which some were proposed by himself. Extensive experiments and a user study surprisingly showed that comparably simple nearest-neighbor techniques usually outperform recent deep learning models across many domains, datasets, and metrics.
Even if long-term preferences are available for the users, recent works indicated that it might still be beneficial to consider the ongoing session, e.g., because a user started the session with a specific intent in mind. The author of this thesis, thus, conducted a systematic statistical analysis to assess what helps recommendations in being effective in such a session-aware scenario. This analysis is based on log data from a fashion retailer and insights were, furthermore, operationalized into novel session-aware recommendation approaches. Matching items of the customer’s ongoing session, reminding him of previously inspected clothes, recommending discounted items, and considering recent trends in the community showed to be particularly effective strategies, not only for item-item recommendation but also in the related scenario of search personalization
Eliciting New Wikipedia Users' Interests via Automatically Mined Questionnaires: For a Warm Welcome, Not a Cold Start
Every day, thousands of users sign up as new Wikipedia contributors. Once
joined, these users have to decide which articles to contribute to, which users
to seek out and learn from or collaborate with, etc. Any such task is a hard
and potentially frustrating one given the sheer size of Wikipedia. Supporting
newcomers in their first steps by recommending articles they would enjoy
editing or editors they would enjoy collaborating with is thus a promising
route toward converting them into long-term contributors. Standard recommender
systems, however, rely on users' histories of previous interactions with the
platform. As such, these systems cannot make high-quality recommendations to
newcomers without any previous interactions -- the so-called cold-start
problem. The present paper addresses the cold-start problem on Wikipedia by
developing a method for automatically building short questionnaires that, when
completed by a newly registered Wikipedia user, can be used for a variety of
purposes, including article recommendations that can help new editors get
started. Our questionnaires are constructed based on the text of Wikipedia
articles as well as the history of contributions by the already onboarded
Wikipedia editors. We assess the quality of our questionnaire-based
recommendations in an offline evaluation using historical data, as well as an
online evaluation with hundreds of real Wikipedia newcomers, concluding that
our method provides cohesive, human-readable questions that perform well
against several baselines. By addressing the cold-start problem, this work can
help with the sustainable growth and maintenance of Wikipedia's diverse editor
community.Comment: Accepted at the 13th International AAAI Conference on Web and Social
Media (ICWSM-2019
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