Heterogeneous information networks (HINs), which are typed graphs with labeled nodes and edges, have attracted tremendous interest from academia and industry. Given two HIN nodes$s$and$t$, and a natural number$k$, we study the discovery of the$k$most important meta paths in real time, which can be used to support friend search, product recommendation, anomaly detection, and graph clustering. In this work, we argue that the shortest path between$s$and$t$may not necessarily be the most important path. As such, we combine several ranking functions, which are based onfrequencyandrarity, to redefine the unified importance function of the meta paths between$s$and$t$. Although this importance function can capture more information, it is very time-consuming to find top-$k$meta paths using this importance function. Therefore, we integrate this importance function into a multi-step framework, which can efficiently filter some impossible meta paths between$s$and$t$. In addition, we combine bidirectional searching algorithm with this framework to further boost the efficiency performance. The experiment on different datasets shows that our proposed method outperforms state-of-the-art algorithms in terms of effectiveness with reasonable response time.

Heterogeneous information networks (HINs), which are typed graphs with labeled nodes and edges, have attracted tremendous interest from academia and industry. Given two HIN nodes s and t, and a natural number k, we study the discovery of the k most important paths in real time. The paths found can be used to support friend search, product recommendation, anomaly detection, and graph clustering. Although related algorithms have been proposed before, they were primarily designed to return the k shortest paths from unlabeled graphs. This leads to two problems: (1) there are often many shortest paths between s and t, and so it is not easy to choose the k best ones; and (2) it is arguable whether a shorter path implies a more crucial one. To address these issues, we study the top-k meta path query for a HIN. A meta path abstracts multiple path instances into a high-level path pattern, thereby giving more insight between two nodes. We further study several ranking functions that evaluate the importance of meta paths based on frequency and rarity, rather than on path length. We propose a solution that seamlessly integrates these functions into an A* search framework. The connectivity experiment on ACM dataset shows that our proposed method outperforms state-of-the-art algorithms.

The long-tail theory for consumer demand implies the need for more accurate personalization technologies to target items to the users who most desire them. A key tenet of personalization is the capacity to model user preferences. Most of the previous work on recommendation and personalization has focused primarily on individual preferences. While some focus on shared preferences between pairs of users, they assume that the same similarity value applies to all items. Here we investigate the notion of “context,” hypothesizing that while two users may agree on their preferences on some items, they may also disagree on other items. To model this, we design probabilistic models for the generation of rating differences between pairs of users across different items. Since this model also involves the estimation of rating differences on unseen items for the purpose of prediction, we further conduct a systematic analysis of matrix factorization and tensor factorization methods in this estimation, and propose a factorization model with a novel objective function of minimizing error in rating differences. Experiments on several real-life rating datasets show that our proposed model consistently yields context-specific similarity values that perform better on a prediction task than models relying on shared preferences.

With greater prevalence of social media, there is an increasing amount of user-generated data revealing consumer preferences for various products and services. Businesses seek to harness this wealth of data to improve their marketing strategies. Bundling, or selling two or more items for one price is a highly-practiced marketing strategy. In this paper, we address the bundle configuration problem from the data-driven perspective. Given a set of items in a seller's inventory, we seek to determine which items should belong to which bundle so as to maximize the total revenue, by mining consumer preferences data. We show that this problem is NP-hard when bundles are allowed to contain more than two items. Therefore, we describe an optimal solution for bundle sizes up to two items, and propose two heuristic solutions for bundles of any larger size. We investigate the effectiveness and the efficiency of the proposed algorithms through experimentations on real-life rating-based preferences data.

Personalization, or customizing the experience of each individual user, is seen as a useful way to navigate the huge variety of choices on the Web today. A key tenet of personalization is the capacity to model user preferences. The paradigm has shifted from that of individual preferences, whereby we look at a user's past activities alone, to that of shared preferences, whereby we model the similarities in preferences between pairs of users (e.g., friends, people with similar interests). However, shared preferences are still too granular, because it assumes that a pair of users would share preferences across all items. We therefore postulate the need to pay attention to "context", which refers to the specific item on which the preferences between two users are to be estimated. In this paper, we propose a generative model for contextual agreement in preferences. For every triplet consisting of two users and an item, the model estimates both the prior probability of agreement between the two users, as well as the posterior probability of agreement with respect to the item at hand. The model parameters are estimated from ratings data. To extend the model to unseen ratings, we further propose several matrix factorization techniques focused on predicting agreement, rather than ratings. Experiments on real-life data show that our model yields context-specific similarity values that perform better on a prediction task than models relying on shared preferences.