1,435 research outputs found
Choice of Metrics used in Collaborative Filtering and their Impact on Recommender Systems
The capacity of recommender systems to make correct predictions is essentially determined by the quality and suitability of the collaborative filtering that implements them. The common memory-based metrics are Pearson correlation and cosine, however, their use is not always the most appropriate or sufficiently justified. In this paper, we analyze these two metrics together with the less common mean squared difference (MSD) to discover their advantages and drawbacks in very important aspects such as the impact when introducing different values of k-neighborhoods, minimization of the MAE error, capacity to carry out a sufficient number of predictions, percentage of correct and incorrect predictions and behavior when attempting to recommend the n-best items. The paper lists the results and practical conclusions that have been obtained after carrying out a comparative study of the metrics based on 135 experiments on the MovieLens database of 100,000 ratios
Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design
It is well known that collaborative filtering (CF) based recommender systems
provide better modeling of users and items associated with considerable rating
history. The lack of historical ratings results in the user and the item
cold-start problems. The latter is the main focus of this work. Most of the
current literature addresses this problem by integrating content-based
recommendation techniques to model the new item. However, in many cases such
content is not available, and the question arises is whether this problem can
be mitigated using CF techniques only. We formalize this problem as an
optimization problem: given a new item, a pool of available users, and a budget
constraint, select which users to assign with the task of rating the new item
in order to minimize the prediction error of our model. We show that the
objective function is monotone-supermodular, and propose efficient optimal
design based algorithms that attain an approximation to its optimum. Our
findings are verified by an empirical study using the Netflix dataset, where
the proposed algorithms outperform several baselines for the problem at hand.Comment: 11 pages, 2 figure
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model
Clicking data, which exists in abundance and contains objective user
preference information, is widely used to produce personalized recommendations
in web-based applications. Current popular recommendation algorithms, typically
based on matrix factorizations, often have high accuracy and achieve good
clickthrough rates. However, diversity of the recommended items, which can
greatly enhance user experiences, is often overlooked. Moreover, most
algorithms do not produce interpretable uncertainty quantifications of the
recommendations. In this work, we propose the Bayesian Mallows for Clicking
Data (BMCD) method, which augments clicking data into compatible full ranking
vectors by enforcing all the clicked items to be top-ranked. User preferences
are learned using a Mallows ranking model. Bayesian inference leads to
interpretable uncertainties of each individual recommendation, and we also
propose a method to make personalized recommendations based on such
uncertainties. With a simulation study and a real life data example, we
demonstrate that compared to state-of-the-art matrix factorization, BMCD makes
personalized recommendations with similar accuracy, while achieving much higher
level of diversity, and producing interpretable and actionable uncertainty
estimation.Comment: 27 page
Cluster searching strategies for collaborative recommendation systems
Cataloged from PDF version of article.In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster- skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures. © 2012 Elsevier Ltd. All rights reserved
RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm
Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems
A Network Resource Allocation Recommendation Method with An Improved Similarity Measure
Recommender systems have been acknowledged as efficacious tools for managing
information overload. Nevertheless, conventional algorithms adopted in such
systems primarily emphasize precise recommendations and, consequently, overlook
other vital aspects like the coverage, diversity, and novelty of items. This
approach results in less exposure for long-tail items. In this paper, to
personalize the recommendations and allocate recommendation resources more
purposively, a method named PIM+RA is proposed. This method utilizes a
bipartite network that incorporates self-connecting edges and weights.
Furthermore, an improved Pearson correlation coefficient is employed for better
redistribution. The evaluation of PIM+RA demonstrates a significant enhancement
not only in accuracy but also in coverage, diversity, and novelty of the
recommendation. It leads to a better balance in recommendation frequency by
providing effective exposure to long-tail items, while allowing customized
parameters to adjust the recommendation list bias
- …