We present a novel strategy for analyzing E-Com search logs called Differential Query Semantic Analysis (DQSA) to discover explicit interpretable knowledge from search logs in the form of a semantic lexicon that makes context-specific mapping from a query segment (word or phrase) to the preferred attribute values of a product. Evaluation on a set of size-related query segments and attribute values shows that DQSA can effectively discover meaningful mappings of size-related query segments to their preferred specific attributes and attributes values in the context of a product type. DQSA has many uses including improvement of E-Com search accuracy by bridging the vocabulary gap, comparative analysis of search intent, and alleviation of the problem of tail queries and products.

Machine learning (ML) has been gaining interest in the metabolic engineering community as a means to automate prediction tasks. In this work, we introduce and study the task of using ML to recommend high-fitness triplet mutants as candidates for wet-lab experiments. We first utilize individual fitness and digenic fitness scores as features and train machine learning models that produce a ranked list, from high to low fitness s cores, f or triplet gene mutants of S. cerevisiae. Then, we incorporate prior metabolic knowledge from an existing gene ontology, by designing a novel graph representation and deducing features that can capture gene similarity and gene interactions. Experimental results show that our proposed gene ontology enriched model, termed TriGORank, improves both performance and explainability.

User simulation is needed for evaluating Interactive Information Retrieval (IIR) Systems. However, for any user simulator to be useful, it must be reliable. In this paper, we propose a novel Tester-based evaluation approach to evaluating the reliability of user simulators, in which we would construct a Tester based on a set of IR systems with an expected performance pattern and apply such a Tester to a user simulator to see if the user simulator would generate the expected performance pattern. We construct multiple Testers and apply them to a set of representative user simulators to empirically study the feasibility and effectiveness of the proposed Tester-based evaluation method. The results show that Tester-based evaluation is a feasible and effective method for evaluating user simulators and selecting reliable ones for evaluating IIR systems.

Learning to Rank is an important framework used in search engines to optimize the combination of multiple features in a single ranking function. In the existing work on learning to rank, such a ranking function is often trained on a large set of different queries to optimize the overall performance on all of them. However, the optimal parameters to combine those features are generally query-dependent, making such a strategy of "one size fits all" non-optimal. Some previous works have addressed this problem by suggesting a query-level adaptive training for learning to rank with promising results. However, previous work has not analyzed the reasons for the improvement. In this paper, we present a Best-Feature Calibration (BFC) strategy for analyzing learning to rank models and use this strategy to examine the benefit of query-level adaptive training. Our results show that the benefit of adaptive training mainly lies in the improvement of the robustness of learning to rank in cases where it does not perform as well as the best single feature.

Lecture slides covering many topics are becoming increasingly available online, but they are scattered, making it a challenge for anyone to instantly access all slides relevant to a learning context. To address this challenge, we propose to create links between those scattered slides to form a Web of Slides (WOS). Using the sequential nature of slides, we present preliminary results of studying how to automatically create a basic link based on similarity of slides as an initial step toward the vision of WOS. We also explore interesting future research directions using different link types and the unique features of slides.

We will demonstrate a prototype system WOSView built based on the vision of the Web of Slides(WOS), which aims to link all the lectures slides so as to facilitate navigation over all the slides. The links can be created at the slide level or at the level of phrases inside a slide, and many types of links can be created. The prototype system we built implements the most basic type of links, which link slides that have similar content and integrates lectures from four different MOOCs. WOSView also supports keyword search, which generates virtual links dynamically. We will demonstrate how the graphical interface of the WOSView enables students to flexibly navigate into slides from different courses and explore related slides using both static and dynamic links and solicit feedback from the community about the vision of WOS.