A Harmonic Extension Approach for Collaborative Ranking

We present a new perspective on graph-based methods for collaborative ranking for recommender systems. Unlike user-based or item-based methods that compute a weighted average of ratings given by the nearest neighbors, or low-rank approximation methods using convex optimization and the nuclear norm, we formulate matrix completion as a series of semi-supervised learning problems, and propagate the known ratings to the missing ones on the user-user or item-item graph globally. The semi-supervised learning problems are expressed as Laplace-Beltrami equations on a manifold, or namely, harmonic extension, and can be discretized by a point integral method. We show that our approach does not impose a low-rank Euclidean subspace on the data points, but instead minimizes the dimension of the underlying manifold. Our method, named LDM (low dimensional manifold), turns out to be particularly effective in generating rankings of items, showing decent computational efficiency and robust ranking quality compared to state-of-the-art methods

From Rankings to Ratings: Rank Scoring Via Active Learning

In this paper we present RaScAL, an active learning approach to predicting real-valued scores for items given access to an oracle and knowledge of the overall item-ranking. In an experiment on six different datasets, we find that RaScAL consistently outperforms the state-of-the-art. The RaScAL algorithm represents one step within a proposed overall system of preference elicitations of scores via pairwise comparisons

Optimizing product recommendations for a try-before-you-buy fashion e-commerce sit

The fashion e-commerce market has experienced a significant growth and more and more customers tend to buy products online, rather than in physical stores. However, after a customer buys a product online, only a fraction of the garments stay in their wardrobe as many items are being returned to the vendor. Due to the absence of physical examination and misleading product descriptions customers struggle to find the right product suitable to their personal preferences. Especially the category of women’s lingerie suffers to a great extend from high return rates. Different sources report that between 70 up to 100% of women wear wrong sized bras. Personalized recommendations through so called recommendation systems play an essential role in e-commerce. This thesis aims to optimize the current product recommendations of a Belgium start-up called CurveCatch that sells women’s lingerie articles online and relies on a try-before-you-buy concept. To predict which products a customer is likely to buy two different personalized deep learning approaches were introduced. Data sparsity was addressed by labeling each unique product per customer and minority classes were synthetically oversampled. The findings demonstrated that recommendation systems are not only relevant for companies operating on a large scale. Rather, they also can be a valuable source of accurate recommendations for start-ups with sparse data. However, results also underlined well-known limitations of recommendation systems. Both models struggled especially when identifying products a customer is likely to buy, while it was rather easy to identify products a customer is not likely to buy.O mercado de e-commerce de moda experimentou um crescimento significativo e cada vez mais os clientes tendem a comprar produtos online, em vez de em lojas físicas. No entanto, muitos itens são devolvidos ao vendedor após a compra online, pois os clientes têm dificuldade em encontrar o produto certo adequado às suas preferências pessoais devido à falta de exame físico e às descrições de produtos enganosas. A categoria de lingerie feminina sofre muito com as altas taxas de devolução. Diferentes fontes relatam que entre 70% e 100% das mulheres usam sutiãs do tamanho errado. As recomendações personalizadas através dos chamados sistemas de recomendação desempenham um papel essencial no e-commerce. Esta tese visa otimizar as atuais recomendações de produtos de uma start-up belga chamada CurveCatch que vende artigos de lingerie feminina online e depende de um conceito de experimente antes de comprar. Para prever quais produtos um cliente é mais propenso a comprar, foram introduzidos dois diferentes abordagens de aprendizado profundo personalizadas. A escassez de dados foi abordada rotulando cada produto único por cliente e as classes minoritárias foram sobreamostradas sinteticamente. Os resultados demonstraram que os sistemas de recomendação também podem ser uma fonte valiosa de recomendação de produtos para start-ups com dados escassos. No entanto, os resultados também sublinharam as bem conhecidas limitações dos sistemas de recomendação. Ambos os modelos lutaram especialmente ao identificar os produtos que um cliente é mais propenso a comprar, enquanto era relativamente fácil identificar os produtos que um cliente não é propenso a comprar

Structuring Wikipedia Articles with Section Recommendations

Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for humans, especially for newcomers or less experienced editors, as it requires significant knowledge about how a well-written article looks for each possible topic. Inspired by this need, the present paper defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it. Our systems can help editors by recommending what sections to add to already existing or newly created Wikipedia articles. Our basic paradigm is to generate recommendations by sourcing sections from articles that are similar to the input article. We explore several ways of defining similarity for this purpose (based on topic modeling, collaborative filtering, and Wikipedia's category system). We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision@10 of about 80% in the human evaluation.Comment: SIGIR '18 camera-read