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Ivan Vendrov

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3 papers

2 author rows

AAAI Conference 2022 Conference Paper

Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities

Filip Radlinski
Craig Boutilier
Deepak Ramachandran
Ivan Vendrov

The ubiquity of recommender systems has increased the need for higher-bandwidth, natural and efficient communication with users. This need is increasingly filled by recommenders that support natural language interaction, often conversationally. Given the inherent semantic subjectivity present in natural language, we argue that modeling subjective attributes in recommenders is a critical, yet understudied, avenue of AI research. We propose a novel framework for understanding different forms of subjectivity, examine various recommender tasks that will benefit from a systematic treatment of subjective attributes, and outline a number of research challenges.

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AAAI Conference 2020 Conference Paper

Gradient-Based Optimization for Bayesian Preference Elicitation

Ivan Vendrov
Tyler Lu
Qingqing Huang
Craig Boutilier

Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and conceptually appealing Bayesian criterion for selecting queries is expected value of information (EVOI). Unfortunately, it is computationally prohibitive to construct queries with maximum EVOI in RSs with large item spaces. We tackle this issue by introducing a continuous formulation of EVOI as a differentiable network that can be optimized using gradient methods available in modern machine learning computational frameworks (e. g. , TensorFlow, PyTorch). We exploit this to develop a novel Monte Carlo method for EVOI optimization, which is much more scalable for large item spaces than methods requiring explicit enumeration of items. While we emphasize the use of this approach for pairwise (or k-wise) comparisons of items, we also demonstrate how our method can be adapted to queries involving subsets of item attributes or “partial items, ” which are often more cognitively manageable for users. Experiments show that our gradientbased EVOI technique achieves state-of-the-art performance across several domains while scaling to large item spaces.

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ICLR Conference 2016 Conference Paper

Order-Embeddings of Images and Language

Ivan Vendrov
Jamie Kiros
Sanja Fidler
Raquel Urtasun

Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval.

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