Replication of recommender systems with impressions

Impressions are a novel data type in Recommender Systems containing the previously-exposed items, i.e., what was shown on-screen. Due to their novelty, the current literature lacks a characterization of impressions, and replications of previous experiments. Also, previous research works have mainly used impressions in industrial contexts or recommender systems competitions, such as the ACM RecSys Challenges. This work is part of an ongoing study about impressions in recommender systems. It presents an evaluation of impressions recommenders on current open datasets, comparing not only the recommendation quality of impressions recommenders against strong baselines, but also determining if previous progress claims can be replicated

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

RELISON: A Framework for Link Recommendation in Social Networks

Link recommendation is an important and compelling problem at the intersection of recommender systems and online social networks. Given a user, link recommenders identify people in the platform the user might be interested in interacting with. We present RELISON, an extensible framework for running link recommendation experiments. The library provides a wide range of algorithms, along with tools for evaluating the produced recommendations. RELISON includes algorithms and metrics that consider the potential effect of recommendations on the properties of online social networks. For this reason, the library also implements network structure analysis metrics, community detection algorithms, and network diffusion simulation functionalities. The library code and documentation is available at https://github.com/ir-uam/RELISON

GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation

Recent advancements in Natural Language Processing (NLP) have led to the development of NLP-based recommender systems that have shown superior performance. However, current models commonly treat items as mere IDs and adopt discriminative modeling, resulting in limitations of (1) fully leveraging the content information of items and the language modeling capabilities of NLP models; (2) interpreting user interests to improve relevance and diversity; and (3) adapting practical circumstances such as growing item inventories. To address these limitations, we present GPT4Rec, a novel and flexible generative framework inspired by search engines. It first generates hypothetical "search queries" given item titles in a user's history, and then retrieves items for recommendation by searching these queries. The framework overcomes previous limitations by learning both user and item embeddings in the language space. To well-capture user interests with different aspects and granularity for improving relevance and diversity, we propose a multi-query generation technique with beam search. The generated queries naturally serve as interpretable representations of user interests and can be searched to recommend cold-start items. With GPT-2 language model and BM25 search engine, our framework outperforms state-of-the-art methods by $75.7\%$ and $22.2\%$ in Recall@K on two public datasets. Experiments further revealed that multi-query generation with beam search improves both the diversity of retrieved items and the coverage of a user's multi-interests. The adaptiveness and interpretability of generated queries are discussed with qualitative case studies

Using implicit feedback for recommender systems: characteristics, applications, and challenges

Recommender systems are software tools to tackle the problem of information overload by helping users to find items that are most relevant for them within an often unmanageable set of choices. To create these personalized recommendations for a user, the algorithmic task of a recommender system is usually to quantify the user's interest in each item by predicting a relevance score, e.g., from the user's current situation or personal preferences in the past. Nowadays, recommender systems are used in various domains to recommend items such as products on e-commerce sites, movies and music on media portals, or people in social networks. To assess the user's preferences, recommender systems proposed in past research often utilized explicit feedback, i.e., deliberately given ratings or like/dislike statements for items. In practice, however, in many of today's application domains of recommender systems this kind of information is not existent. Therefore, recommender systems have to rely on implicit feedback that is derived from the users' behavior and interactions with the system. This information can be extracted from navigation or transaction logs. Using implicit feedback leads to new challenges and open questions regarding, for example, the huge amount of signals to process, the ambiguity of the feedback, and the inevitable noise in the data. This thesis by publication explores some of these challenges and questions that have not been covered in previous research. The thesis is divided into two parts. In the first part, the thesis reviews existing works on implicit feedback and recommender systems that exploit these signals, especially in the Social Information Access domain, which utilizes the "community wisdom" of the social web for recommendations. Common application scenarios for implicit feedback are discussed and a categorization scheme that classifies different types of observable user behavior is established. In addition, state-of-the-art algorithmic approaches for implicit feedback are examined that, e.g., interpret implicit signals directly or convert them to explicit ratings to be able to use "classic" recommendation approaches that were designed for explicit feedback. The second part of the thesis comprises some of the author's publications that deal with selected challenges of implicit feedback based recommendations. These contain (i) a specialized learning-to-rank algorithm that can differentiate different levels of interest indicator strength in implicit signals, (ii) contextualized recommendation techniques for the e-commerce domain that adapt product suggestions to customers' current short-term goals as well as their long-term preferences, and (iii) intelligent reminding approaches that aim at the re-discovery of relevant items in a customer's browsing history. Furthermore, the last paper of the thesis provides an in-depth analysis of different biases of various recommendation algorithms. Especially the popularity bias, the tendency to recommend mostly popular items, can be problematic in practical settings and countermeasures to reduce this bias are proposed