1,336 research outputs found
Pareto-based Multi-Objective Recommender System with Forgetting Curve
Recommender systems with cascading architecture play an increasingly
significant role in online recommendation platforms, where the approach to
dealing with negative feedback is a vital issue. For instance, in short video
platforms, users tend to quickly slip away from candidates that they feel
aversive, and recommender systems are expected to receive these explicit
negative feedbacks and make adjustments to avoid these recommendations.
Considering recency effect in memories, we propose a forgetting model based on
Ebbinghaus Forgetting Curve to cope with negative feedback. In addition, we
introduce a Pareto optimization solver to guarantee a better trade-off between
recency and model performance. In conclusion, we propose Pareto-based
Multi-Objective Recommender System with forgetting curve (PMORS), which can be
applied to any multi-objective recommendation and show sufficiently superiority
when facing explicit negative feedback. We have conducted evaluations of PMORS
and achieved favorable outcomes in short-video scenarios on both public dataset
and industrial dataset. After being deployed on an online short video platform
named WeChat Channels in May, 2023, PMORS has not only demonstrated promising
results for both consistency and recency but also achieved an improvement of up
to +1.45% GMV
A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups
In the collaborative filtering recommender systems (CFRS) field, recommendation to group of users is mainly focused on stablished, occasional or random groups. These groups have a little number of users: relatives, friends, colleagues, etc. Our proposal deals with large numbers of automatically detected groups. Marketing and electronic commerce are typical targets of large homogenous groups. Large groups present a major difficulty in terms of automatically achieving homogeneity, equilibrated size and accurate recommendations. We provide a method that combines diverse machine learning algorithms in an original way: homogeneous groups are detected by means of a clustering based on hidden factors instead of ratings. Predictions are made using a virtual user model, and virtual users are obtained by performing a hidden factors aggregation. Additionally, this paper selects the most appropriate dimensionality reduction for the explained RS aim. We conduct a set of experiments to catch the maximum cumulative deviation of the ratings information. Results show an improvement on recommendations made to large homogeneous groups. It is also shown the desirability of designing specific methods and algorithms to deal with automatically detected groups
Python Library for Consumer Decision Support System with Automatic Identification of Preferences
The development of information systems (IS) has increased in the e-commerce field. The need for continuous improvement of decision support systems implies the integration of multiple methodologies such as expert knowledge, data mining, big data, artificial intelligence, and multicriteria decision analysis (MCDA) methods. Artificial intelligence algorithms have proven their effectiveness as an engine for data-driven information systems. MCDA methods demonstrated usefulness in domains dealing with multiple dimensions. One of the most critical points of any MCDA procedure is criteria weighting using subjective or objective methods. However, both approaches have several limitations when there is a need to map the preferences of unavailable experts. EVO-SPOTIS library integrating a stochastic evolutionary algorithm with the MCDA method, introduced in this paper, attempts to address this problem. In this approach, the Differential Evolution (DE) algorithm is used to identify decision-makers’ preferences based on datasets evaluated by experts in the past. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) method is used to compute the DE objective function’s values and perform the final evaluation of alternatives using the identified weights. Results confirm the high potential of the library for identification preferences and modeling customer behavior
TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators
© 2018 Elsevier B.V. In recent years, an increase in group activities on websites has led to greater demand for highly-functional group recommender systems. The goal of group recommendation is to capture and distill the preferences of each group member into a single recommendation list that meets the needs of all group members. Existing aggregation functions perform well in harmonious and congruent scenarios, but tend not to generate satisfactory results when group members hold conflicting preferences. Moreover, most of current studies improve group recommendation only based on a single aggregation strategy and explicit trust information is still ignored in group recommender systems. Motivated by these concerns, this paper presents TruGRC, a novel Trust-aware Group Recommendation method with virtual Coordinators, that combines two different aggregation strategies: result aggregation and profile aggregation. As each individual's preferences are modeled, a virtual user is built as a coordinator to represent the profile aggregation strategy. This coordinator provides a global view of the preferences for all group members by interacting with each user to resolve conflicting preferences. Then, we also model the impact from group members to the virtual coordinator in accordance with personal social influence inferred by trust information on social networks. Group preferences can be easily generated by the average aggregation method under the effect of the virtual coordinator. Experimental results on two benchmark datasets with a range of different group sizes show that TruGRC method has significant improvements compared to other state-of-the-art methods
ICMRec: Item Cluster-Wise Multi-Objective Optimization for Unbiased Recommendation
The traditional observed data used to train the recommender model suffers
from severe bias issues (e.g., exposure bias, popularity bias). Interactions of
a small fraction of head items account for almost the whole training data. The
normal training paradigm from such biased data tends to repetitively generate
recommendations from the head items, which further exacerbates the biases and
affects the exploration of potentially interesting items from the niche set. In
this work, distinct from existing methods, we innovatively explore the central
theme of unbiased recommendation from an item cluster-wise multi-objective
optimization perspective. Aiming to balance the learning on various item
clusters that differ in popularity during the training process, we characterize
the recommendation task as an item cluster-wise multi-objective optimization
problem. To this end, we propose a model-agnostic framework namely Item
Cluster-Wise Multi-Objective Recommendation (ICMRec) for unbiased
recommendation. In detail, we define our item cluster-wise optimization target
that the recommender model should balance all item clusters that differ in
popularity. Thus we set the model learning on each item cluster as a unique
optimization objective. To achieve this goal, we first explore items'
popularity levels from a novel causal reasoning perspective. Then, we devise
popularity discrepancy-based bisecting clustering to separate the discriminated
item clusters. Next, we adaptively find the overall harmonious gradient
direction for multiple item cluster-wise optimization objectives from a
Pareto-efficient solver. Finally, in the prediction stage, we perform
counterfactual inference to further eliminate the impact of user conformity.
Extensive experimental results demonstrate the superiorities of ICMRec on
overall recommendation performance and biases elimination. Codes will be
open-source upon acceptance
EvoRecSys: Evolutionary framework for health and well-being recommender systems
Hugo Alcaraz-Herrera's PhD is supported by The Mexican Council of Science and Technology (Consejo Nacional de Ciencia y Tecnologia - CONACyT).In recent years, recommender systems have been employed in domains like ecommerce,
tourism, and multimedia streaming, where personalising users’ experience
based on their interactions is a fundamental aspect to consider. Recent recommender
system developments have also focused on well-being, yet existing solutions have
been entirely designed considering one single well-being aspect in isolation, such
as a healthy diet or an active lifestyle. This research introduces EvoRecSys, a novel
recommendation framework that proposes evolutionary algorithms as the main recommendation
engine, thereby modelling the problem of generating personalised
well-being recommendations as a multi-objective optimisation problem. EvoRecSys
captures the interrelation between multiple aspects of well-being by constructing configurable
recommendations in the form of bundled items with dynamic properties. The preferences and a predefined well-being goal by the user are jointly considered.
By instantiating the framework into an implemented model, we illustrate the use of
a genetic algorithm as the recommendation engine. Finally, this implementation has
been deployed as a Web application in order to conduct a users’ study.Consejo Nacional de Ciencia y Tecnologia (CONACyT
Fairness and Diversity in Recommender Systems: A Survey
Recommender systems are effective tools for mitigating information overload
and have seen extensive applications across various domains. However, the
single focus on utility goals proves to be inadequate in addressing real-world
concerns, leading to increasing attention to fairness-aware and diversity-aware
recommender systems. While most existing studies explore fairness and diversity
independently, we identify strong connections between these two domains. In
this survey, we first discuss each of them individually and then dive into
their connections. Additionally, motivated by the concepts of user-level and
item-level fairness, we broaden the understanding of diversity to encompass not
only the item level but also the user level. With this expanded perspective on
user and item-level diversity, we re-interpret fairness studies from the
viewpoint of diversity. This fresh perspective enhances our understanding of
fairness-related work and paves the way for potential future research
directions. Papers discussed in this survey along with public code links are
available at
https://github.com/YuyingZhao/Awesome-Fairness-and-Diversity-Papers-in-Recommender-Systems
Implicit feedback-based group recommender system for internet of things applications
With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened. As a result, recommender systems in IoT-based social media need to be developed oriented to groups of users rather than individual users. However, existing methods were highly dependent on explicit preference feedbacks, ignoring scenarios of implicit feedbacks. To remedy such gap, this paper proposes an implicit feedback-based group recommender system using probabilistic inference and non-cooperative game (GREPING) for IoT-based social media. Particularly, unknown process variables can be estimated from observable implicit feedbacks via Bayesian posterior probability inference. In addition, the globally optimal recommendation results can be calculated with the aid of non-cooperative game. Two groups of experiments are conducted to assess the GREPING from two aspects: efficiency and robustness. Experimental results show obvious promotion and considerable stability of the GREPING compared to baseline methods. © 2020 IEEE
- …