SAGA: A Submodular Greedy Algorithm For Group Recommendation Machine Learning

In this paper, we propose a unified framework and an algorithm for the problem of group recommendation where a fixed number of items or alternatives can be recommended to a group of users. The problem of group recommendation arises naturally in many real world contexts, and is closely related to the budgeted social choice problem studied in economics. We frame the group recommendation problem as choosing a subgraph with the largest group consensus score in a completely connected graph defined over the item affinity matrix. We propose a fast greedy algorithm with strong theoretical guarantees, and show that the proposed algorithm compares favorably to the state-of-the-art group recommendation algorithms according to commonly used relevance and coverage performance measures on benchmark dataset.

Re-ranking Based Diversification: A Unifying View Machine Learning

We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions. We study the optimality of such algorithms in terms of the `total curvature'. We also show that by adjusting the hyperparameter of the concave/linear composition to trade-off relevance and diversity, if any, one is in fact tuning the `total curvature' of the function for relevance-diversity trade-off.

Risk Aware Ranking for Top-$k$ Recommendations Machine Learning

Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items. In practical settings, a ranked list of top-$k$ items from the estimated preference order is recommended to the end user in the decreasing order of preference for final consumption. We analyze this model and observe that such a ranking model results in suboptimal performance when the payoff associated with the recommended items is different. We propose a novel and very efficient algorithm for the preference ranking considering the uncertainty regarding the payoffs of the items. Once the preference scores for the users are obtained using any preference learning algorithm, we show that ranking the items using a risk seeking utility function results in the best ranking performance.

Differential Neighborhood Selection In Memory-Based Group Recommender Systems

AAAI Conferences

As recommender systems have become commonplace to support individual decision making, a need has also been recognized for systems that tailor and provide recommendations to a group of users together rather than individuals alone. Group recommender research to date has focused on evaluating strategies for aggregating profiles of group members to form a consolidated group profile or for aggregating recommendations to individual group members as a consolidated group recommendation list. This paper presents a novel neighborhood selection approach for group recommendation in the context of a neighborhood-based Collaborative Filtering system. We evaluate the performance of this approach with respect to group characteristics such as size and group member similarity. Results show that this approach can result in more accurate predictions for the group, particularly for groups that are more homogenous.

Optimal Greedy Diversity for Recommendation

AAAI Conferences

The need for diversification manifests in various recommendation use cases. In this work, we propose a novel approach to diversifying a list of recommended items, which maximizes the utility of the items subject to the increase in their diversity. From a technical perspective, the problem can be viewed as maximization of a modular function on the polytope of a submodular function, which can be solved optimally by a greedy method. We evaluate our approach in an offline analysis, which incorporates a number of baselines and metrics, and in two online user studies. In all the experiments, our method outperforms the baseline methods.