EVALUATION OF ANTIDIABETIC POTENTIAL OF ROOTS AND STEMS OF G. ARBOREA

Objectives: Roots and stems of Gmelina arborea Roxb. (Verbenaceae) are used in many Ayurvedic (Dashmula) and herbal formulations (Diabecon) and reported to possess hypoglycemic activities which formed the basis of the present investigation. Methanolic extracts of stems and roots of Gmelina arborea were evaluated for antidiabetic activity in diabetic rats.Methods: Total phenolics and flavonoids were estimated in methanolic, aqueous and ethyl acetate extracts of roots and stems of G. arborea. Antidiabetic activity of methanolic extracts of stems and roots of G. arborea was investigated in streptozotocin induced diabetic rats for 21 days at two dose levels (250 and 500 mg/kg) with glibenclamide (0.25 mg/kg) used as a standard drug.Results: Methanolic extracts of stems and roots showed considerable amount of phenolics and flavonoids compared to aqueous and ethyl acetate extracts. It also showed significant (p<0.001) reduction in fasting blood glucose level in both normal and diabetic rats. Methanolic extract of stems and roots at 500 mg/kg showed significant decrease (54.69% and 45.31% respectively) in blood glucose levels when compared to the standard. In addition, change in body weight, serum lipid profile and GHb (whole blood) levels were also compared amongst various groups treated with different extracts and significant antidiabetic activity observed might be attributed to appreciable amount of phenolics and flavonoids in methanolic extract of roots and stems. The results clearly indicate potential antidiabetic effects of roots and stems of this plant.Conclusion: These findings support the use of G. arborea in herbal formulations for diabetes and will be helpful to explore isolation and identification of bioactives from this drug to manage diabetes and related complications.Â

Learning To Rank Diversely At Airbnb

Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.Comment: Search ranking, Diversity, e-commerc

Optimizing Airbnb Search Journey with Multi-task Learning

At Airbnb, an online marketplace for stays and experiences, guests often spend weeks exploring and comparing multiple items before making a final reservation request. Each reservation request may then potentially be rejected or cancelled by the host prior to check-in. The long and exploratory nature of the search journey, as well as the need to balance both guest and host preferences, present unique challenges for Airbnb search ranking. In this paper, we present Journey Ranker, a new multi-task deep learning model architecture that addresses these challenges. Journey Ranker leverages intermediate guest actions as milestones, both positive and negative, to better progress the guest towards a successful booking. It also uses contextual information such as guest state and search query to balance guest and host preferences. Its modular and extensible design, consisting of four modules with clear separation of concerns, allows for easy application to use cases beyond the Airbnb search ranking context. We conducted offline and online testing of the Journey Ranker and successfully deployed it in production to four different Airbnb products with significant business metrics improvements.Comment: Search Ranking, Recommender Systems, User Search Journey, Multi-task learning, Two-sided marketplac