Utilizing Human Memory Processes to Model Genre Preferences for Personalized Music Recommendations

In this paper, we introduce a psychology-inspired approach to model and predict the music genre preferences of different groups of users by utilizing human memory processes. These processes describe how humans access information units in their memory by considering the factors of (i) past usage frequency, (ii) past usage recency, and (iii) the current context. Using a publicly available dataset of more than a billion music listening records shared on the music streaming platform Last.fm, we find that our approach provides significantly better prediction accuracy results than various baseline algorithms for all evaluated user groups, i.e., (i) low-mainstream music listeners, (ii) medium-mainstream music listeners, and (iii) high-mainstream music listeners. Furthermore, our approach is based on a simple psychological model, which contributes to the transparency and explainability of the calculated predictions.Comment: Dominik Kowald and Elisabeth Lex contributed equally to this wor

Factual and Personalized Recommendations using Language Models and Reinforcement Learning

Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences. While traditional RSs rely on implicit user feedback signals, conversational RSs interact with users in natural language. In this work, we develop a comPelling, Precise, Personalized, Preference-relevant language model (P4LM) that recommends items to users while putting emphasis on explaining item characteristics and their relevance. P4LM uses the embedding space representation of a user's preferences to generate compelling responses that are factually-grounded and relevant w.r.t. the user's preferences. Moreover, we develop a joint reward function that measures precision, appeal, and personalization, which we use as AI-based feedback in a reinforcement learning-based language model framework. Using the MovieLens 25M dataset, we demonstrate that P4LM delivers compelling, personalized movie narratives to users

Explanations in Music Recommender Systems in a Mobile Setting

Revised version: some spelling errors corrected.Every day, millions of users utilize their mobile phones to access music streaming services such as Spotify. However, these `black boxes’ seldom provide adequate explanations for their music recommendations. A systematic literature review revealed that there is a strong relationship between moods and music, and that explanations and interface design choices can effect how people perceive recommendations just as much as algorithm accuracy. However, little seems to be known about how to apply user-centric design approaches, which exploit affective information to present explanations, to mobile devices. In order to bridge these gaps, the work of Andjelkovic, Parra, & O’Donovan (2019) was extended upon and applied as non-interactive designs in a mobile setting. Three separate Amazon Mechanical Turk studies asked participants to compare the same three interface designs: baseline, textual, and visual (n=178). Each survey displayed a different playlist with either low, medium, or high music popularity. Results indicate that music familiarity may or may not influence the need for explanations, but explanations are important to users. Both explanatory designs fared equally better than the baseline, and the use of affective information may help systems become more efficient, transparent, trustworthy, and satisfactory. Overall, there does not seem to be a `one design fits all’ solution for explanations in a mobile setting.Master's Thesis in Information ScienceINFO390MASV-INFOMASV-IK

Designing Exploratory Search Systems that Stimulate Memory and Reduce Cognitive Load

From music fans finding new songs in a genre, graphic designers brainstorming ways to depict a message, and journalists scrutinizing documents for angles, people often conduct exploratory searches to understand complex topics. In contrast to traditional search, which is done to quickly answer simple questions, exploratory search is an iterative learning process that involves understanding an information space in order to find useful pieces of information. Exploratory search is composed of two, closely-related sub-processes: (1) information foraging, choosing sources and collecting information, and (2) sensemaking, organizing this information into a mental framework. Both of these sub-processes are cognitively taxing and heavily rely on our memory. For information foraging, users need to read long, complex resources and recognize useful pieces of information. For sensemaking, as users encounter more information, it becomes harder to relate new information to their current knowledge. The spreading activation theory of memory purports that the information we encounter materializes in our working memory, which spreads activation into our long-term memory, enabling us to recall related semantic information to make sense of newly found information. From this theory, this thesis introduces three strategies for creating organizations that better stimulate memory: (1) constructing overviews that are association networks that mimic our memory's structure, (2) incorporating our prior knowledge in these overviews, and (3) providing concrete information to help us make sense of abstract ideas. This thesis demonstrates how to employ these strategies through three exploratory search systems across three domains: (A) SymbolFinder helps graphic designers explore visual symbols for abstract concepts, (B) TastePaths helps music fans explore artists within a genre, and (C) AngleKindling supports journalists explore story angles for a press release. Through this body of work, I demonstrate that by designing exploratory search systems to stimulate our memory, we can make acquiring and making sense of knowledge less cognitively demanding