Most existing personalization systems promote items that match a user’s previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks.

Recommender systems use big data methods, and are widely used in various social-network, e-commerce, and content platforms. With their increased relevance, online platforms and developers are in need of better ways to choose the systems that are most suitable for their use-cases. At the same time, the research literature on recommender systems describes a multitude of measures to evaluate the performance of different algorithms. For the end-user however, the large number of available measures do not provide much help in deciding which algorithm to deploy. Some of the measures are correlated, while others deal with different aspects of recommendation performance like accuracy and coverage. To address this problem, we propose a novel benchmarking framework that mixes different evaluation measures in order to rank the recommender systems on each benchmark dataset, separately. Additionally, our approach discovers sets of correlated measures as well as sets of evaluation measures that are least correlated. We investigate the robustness of the proposed methodology using published results from an experimental study involving multiple big datasets and evaluation measures. Our work provides a general framework that can handle an arbitrary number of evaluation measures and help end-users rank the systems available to them.

In this paper we explore the LanguaL hierarchy, which is a standard used to describe foods, in order to see if different food standards that are included in this hierarchy are linked together properly. For this reason, we have trained a vector representation (i.e. embedding) for each food product that is a part of the hierarchy and have found the most similar products for a subset of products. The results have shown that the LanguaL hierarchy consists of various food standards that are connected in one hierarchy, but without semantically linking the standards together. Such results indicate that further efforts should be made to link all these standards completely in order to provide a unified system for describing and standardizing food products.

Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions -- bi-directional effects between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate embeddings on typical link prediction tasks and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate embeddings from a new perspective -- giving explanations for predicted triples, which is important for real applications. In this work, an explanation for a triple is regarded as a reliable closed-path between the head and the tail entity. Compared to other baselines, we show experimentally that CrossE, benefiting from interaction embeddings, is more capable of generating reliable explanations to support its predictions.

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.

In most existing recommender systems, implicit or explicit interactions are treated as positive links and all unknown interactions are treated as negative links. The goal is to suggest new links that will be perceived as positive by users. However, as signed social networks and newer content services become common, it is important to distinguish between positive and negative preferences. Even in existing applications, the cost of a negative recommendation could be high when people are looking for new jobs, friends, or places to live.

Recommender systems form the backbone of many interactive systems. They incorporate user feedback to personalize the user experience typically via personalized recommendation lists. As users interact with a system, an increasing amount of data about a user’s preferences becomes available, which can be leveraged for improving the systems’ performance. Incorporating these new data into the underlying recommendation model is, however, not always straightforward. Many models used by recommender systems are computationally expensive and, therefore, have to perform offline computations to compile the recommendation lists. For interactive applications, it is desirable to be able to update the computed values as soon as new user interaction data is available: updating recommendations in interactive time using new feedback data leads to better accuracy and increases the attraction of the system to the users. Additionally, there is a growing consensus that accuracy alone is not enough and user satisfaction is also dependent on diverse recommendations. In this work, we tackle this problem of updating personalized recommendation lists for interactive applications in order to provide both accurate and diverse recommendations. To that end, we explore algorithms that exploit random walks as a sampling technique to obtain diverse recommendations without compromising on efficiency and accuracy. Specifically, we present a novel graph vertex ranking recommendation algorithm called RP 3 β that reranks items based on three-hop random walk transition probabilities. We show empirically that RP 3 β provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present approximate versions of RP 3 β and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with an increasing number of samples. To obtain interactively updatable recommendations, we additionally show how our algorithm can be extended for online updates at interactive speeds. The underlying random walk sampling technique makes it possible to perform the updates without having to recompute the values for the entire dataset. In an empirical evaluation with three real-world datasets, we show that RP 3 β provides highly accurate and diverse recommendations that can easily be updated with newly gathered information at interactive speeds (≪ 100 ms ).

User satisfaction is often dependent on providing accurate and diverse recommendations. In this paper, we explore scalable algorithms that exploit random walks as a sampling technique to obtain diverse recommendations without compromising on accuracy. Specifically, we present a novel graph vertex ranking recommendation algorithm called RP^3_beta that re-ranks items based on 3-hop random walk transition probabilities. We show empirically, that RP^3_beta provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present scalable approximate versions of RP^3_beta and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with increasing number of samples.

Most Semantic Web applications rely on querying graphs, typically by using SPARQL with a triple store. Increasingly, applications also analyze properties of the graph structure to compute statistical inferences. The current Semantic Web infrastructure, however, does not efficiently support such operations. This forces developers to extract the relevant data for external statistical post-processing. In this paper we propose to rethink query execution in a triple store as a highly parallelized asynchronous graph exploration on an active index data structure. This approach also allows to integrate SPARQL-querying with the sampling of graph properties.