The Case for Graph-Based Recommendations

Recommender systems have been intensively used to create personalised profiles, which enhance the user experience. In certain areas, such as e-learning, this approach is short-sighted, since each student masters each concept through different means. The progress from one concept to the next, or from one lesson to another, does not necessarily follow a fixed pattern. Given these settings, we can no longer use simple structures (vectors, strings, etc.) to represent each user's interactions with the system, because the sequence of events and their mapping to user's intentions, build up into more complex synergies. As a consequence, we propose a graph-based interpretation of the problem and identify the challenges behind (a) using graphs to model the users' journeys and hence as the input to the recommender system, and (b) producing recommendations in the form of graphs of actions to be taken

Evaluation of Kermeta for Solving Graph-based Problems

Kermeta is a meta-language for specifying the structure and behavior of graphs of interconnected objects called models. In this paper,\ud we show that Kermeta is relatively suitable for solving three graph-based\ud problems. First, Kermeta allows the specification of generic model\ud transformations such as refactorings that we apply to different metamodels\ud including Ecore, Java, and Uml. Second, we demonstrate the extensibility\ud of Kermeta to the formal language Alloy using an inter-language model\ud transformation. Kermeta uses Alloy to generate recommendations for\ud completing partially specified models. Third, we show that the Kermeta\ud compiler achieves better execution time and memory performance compared\ud to similar graph-based approaches using a common case study. The\ud three solutions proposed for those graph-based problems and their\ud evaluation with Kermeta according to the criteria of genericity,\ud extensibility, and performance are the main contribution of the paper.\ud Another contribution is the comparison of these solutions with those\ud proposed by other graph-based tools

A graph-based approach for sustainable walking tour recommendations: The case of Lisbon overcrowding

Motivation: Mass tourism brought problems of carrying capacity in city management. More and more tourists flock to the most famous zones, thereby causing overcrowding situations, while other sustainable points of interest (POIs) are under-visited. Goal: Allow local tourism managing authorities to assemble a database of georeferenced sustainable POIs. Then, combine the latter with local crowding data and implement a walking tour recommender system. Proposal: A web platform to experts adds, in an intuitive way by using a map,POIs with sustainable data. Creating a new database of Lisbon (case of study) sustainable POIs. Implement a tour generator graph-algorithm that receives: user preferences, tour constraints, sustainable POIs and crowd data. Providing a customize tour, that obeys the domain constraints, suggests sustainable POIs and avoids the more crowded areas. Solving a multicriteria shortest path problem. Conclusion: Evidence is provided on the feasibility of computing walking tour recommendations, meeting multiple and complex constraints, namely by promoting sustainability and mitigating crowding, using a graph search algorithm.Motivação: O turismo em massa trouxe problemas de controlo da capacidade de carga na gestão das cidades. Os turistas, em número crescente, aglomeram-se nas zonas mais famosas, causando aí situações de sobrelotação, enquanto outros pontos de interesse (POIs, em Inglês) sustentáveis são sub-visitados. Objetivo: Permitir que as autoridades gestoras do turismo local montem uma base de dados de POIs sustentáveis, georreferenciados. Em seguida, combinar estes últimos com os dados de aglomeração local e implementar um sistema de recomendação de passeios turísticos pedestres. Proposta: Uma plataforma web para que o especialistas adicionem, de forma intuitiva através de um mapa, os pontos de interesse sustentáveis. Implementar um algoritmo de grafos, que gera caminhos e que recebe: as preferências do utilizador, as restrições do domínio do caminho, pontos de interesse sustentáveis e dados de congestionamento. Fornecendo assim, um caminho personalizado que obdece às restrições, sugere pontos de interesse sustentáveis e evita as áreas mais movimentadas. Deste modo, resolve o problema do caminho mais curto com multicriterias. Conclusão: São fornecidas evidências sobre a viabilidade de computar recomendações de passeios turísticos a pé, atendendo a restrições múltiplas e complexas, nomeadamente promovendo a sustentabilidade e mitigando a superlotação, usando um algoritmo de pesquisa em grafos

Recommendation Subgraphs for Web Discovery

Recommendations are central to the utility of many websites including YouTube, Quora as well as popular e-commerce stores. Such sites typically contain a set of recommendations on every product page that enables visitors to easily navigate the website. Choosing an appropriate set of recommendations at each page is one of the key features of backend engines that have been deployed at several e-commerce sites. Specifically at BloomReach, an engine consisting of several independent components analyzes and optimizes its clients' websites. This paper focuses on the structure optimizer component which improves the website navigation experience that enables the discovery of novel content. We begin by formalizing the concept of recommendations used for discovery. We formulate this as a natural graph optimization problem which in its simplest case, reduces to a bipartite matching problem. In practice, solving these matching problems requires superlinear time and is not scalable. Also, implementing simple algorithms is critical in practice because they are significantly easier to maintain in production. This motivated us to analyze three methods for solving the problem in increasing order of sophistication: a sampling algorithm, a greedy algorithm and a more involved partitioning based algorithm. We first theoretically analyze the performance of these three methods on random graph models characterizing when each method will yield a solution of sufficient quality and the parameter ranges when more sophistication is needed. We complement this by providing an empirical analysis of these algorithms on simulated and real-world production data. Our results confirm that it is not always necessary to implement complicated algorithms in the real-world and that very good practical results can be obtained by using heuristics that are backed by the confidence of concrete theoretical guarantees

An Incremental GraphBLAS Solution for the 2018 TTC Social Media Case Study

Graphs are increasingly important for modelling and analysing connected data sets. Traditionally, graph analytical tools targeted global fixed-point computations, while graph databases focused on simpler transactional read operations such as retrieving the neighbours of a node. However, recent applications of graph processing (such as financial fraud detection and serving personalized recommendations) often necessitate a mix of the two workload profiles. A potential approach to tackle these complex workloads is to formulate graph algorithms in the language of linear algebra. To this end, the recent GraphBLAS standard defines a linear algebraic graph computational model and an API for implementing such algorithms. To investigate its usability and efficiency, we have implemented a GraphBLAS solution for the "Social Media" case study of the 2018 Transformation Tool Contest. This paper presents our solution along with an incrementalized variant to improve its runtime for repeated evaluations. Preliminary results show that the GraphBLAS-based solution is competitive but implementing it requires significant development efforts

Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation

Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability. Recently, some researchers have started to question attention-based explainability because the attention weights are unstable for different reproductions, and they may not always align with human intuition. Inspired by the counterfactual reasoning from causality learning theory, we propose a novel explainable framework targeting path-based recommendations, wherein the explainable weights of paths are learned to replace attention weights. Specifically, we design two counterfactual reasoning algorithms from both path representation and path topological structure perspectives. Moreover, unlike traditional case studies, we also propose a package of explainability evaluation solutions with both qualitative and quantitative methods. We conduct extensive experiments on three real-world datasets, the results of which further demonstrate the effectiveness and reliability of our method.Comment: accepted by TKD

Recommending Related Products Using Graph Neural Networks in Directed Graphs

Related product recommendation (RPR) is pivotal to the success of any e-commerce service. In this paper, we deal with the problem of recommending related products i.e., given a query product, we would like to suggest top-k products that have high likelihood to be bought together with it. Our problem implicitly assumes asymmetry i.e., for a phone, we would like to recommend a suitable phone case, but for a phone case, it may not be apt to recommend a phone because customers typically would purchase a phone case only while owning a phone. We also do not limit ourselves to complementary or substitute product recommendation. For example, for a specific night wear t-shirt, we can suggest similar t-shirts as well as track pants. So, the notion of relatedness is subjective to the query product and dependent on customer preferences. Further, various factors such as product price, availability lead to presence of selection bias in the historical purchase data, that needs to be controlled for while training related product recommendations model. These challenges are orthogonal to each other deeming our problem nontrivial. To address these, we propose DAEMON, a novel Graph Neural Network (GNN) based framework for related product recommendation, wherein the problem is formulated as a node recommendation task on a directed product graph. In order to capture product asymmetry, we employ an asymmetric loss function and learn dual embeddings for each product, by appropriately aggregating features from its neighborhood. DAEMON leverages multi-modal data sources such as catalog metadata, browse behavioral logs to mitigate selection bias and generate recommendations for cold-start products. Extensive offline experiments show that DAEMON outperforms state-of-the-art baselines by 30-160% in terms of HitRate and MRR for the node recommendation task.Comment: This work was accepted in ECML PKDD 202

Interactive Computer Training for Graphing Embedded Phase Change Lines in Microsoft Excel

Graphing data is an essential skill for those who are implementing behavior analytic interventions. The current investigation evaluated the effects of an interactive computer training on graphing skills using a multiple-baseline design across four participants. The computer training included 4 modules, based on a modified version of the embedding phase change task analysis from Deochand, Costello, & Fuqua, (2015). Each module included instructions, video demonstrations, opportunities to practice, and prompts to self-monitor performance. Participants completed modules independently. During baseline sessions, participants were given a data set, case scenario, and model graph. Participants had up to 20 minutes to create a graph that included components in the model. Post-training sessions were identical to baseline except that participants were able to use self-monitoring checklists task analyses during sessions. Results indicated that all participants were able to create graphs to mastery criteria. During a two-week maintenance check, participants were able to create a graph to mastery only during the session where notes were available. Participants completed the training in an average of 1 hour, 43 minutes. Future directions, and recommendations for using computerized instruction to teach graphing skills will be discussed

Integral assessment of the effectiveness of investment projects on the basis of econometric methods

The objective of the article is the solution of the problem of theoretical foundation and methodical support of the formation of an integrated evaluation of investment projects on the basis of econometric methods. The result of the study is the development of the recommendations for the evaluation of the effectiveness of investment projects with the use of econometric methods that help to influence the value of the net present impact, profitability index, internal rate of return, payback period of the most significant factors that are crucial for the project both at the stage of development and at the stage of implementation. These factors have been determined on the basis of multifactorial regression models. In the following paper we present a novel approach to unstructured data processing by imposing a hierarchical graph-based structure on the data and decomposing it into separate subgraphs according to optimization criteria. In the scope of the paper we also consider the problem of automatic classification of textual data for the synthesizing the hierarchical data structure. The proposed approach uses textual information on the first stage to classify ideas, innovations, and objects of intellectual property (OIPs) to construct a multilayered graph. Numerical criteria are used to decompose constructed graph into separate subgraphs. In the scope of the research we apply the developed approach to the innovative ideas in a management case study.peer-reviewe