ISLA An Algorithmic Approach to Assisted Narrative Planning and Assembly
Djyron Sarroza, Institute of Computer Science, University of the Philippines Los Baños, Philippines
Intelligent Story Layout Assistant (ISLA) is a forward-chaining narrative planner based on Stephen Ware’s GLAIVE. It constructs story layouts that achieve the author’s goals while making sure that most steps in the plan have clear motivations. These layouts, or solution plans, are based on a handcrafted knowledge-base of story universe elements. ISLA provides the data structures needed to potentially further assist the author in fleshing out the produced story layout.
Artificial Intelligence, Narrative Planner
A PERSONALIZED PRODUCT RECOMMENDATION SYSTEM BASED ON VECTOR SPACE MODELS
Kiruparan Balachandran, Malankandage Ganeesha Sandeepani, and Thilina Randeniya, Innovation Quotient (Pvt) Ltd, Colombo, Sri Lanka
Recommendation engines are integrated with e-commerce platforms to provide better customer experience and improve their sales. Most e-commerce platforms struggle to achieve these milestones because of their weakly-built recommendation models. This research focuses on improving the process of building a recommendation engine. Available approaches for constructing recommendation engines are limited in various aspects. Such limitations include: (1) considering customers’ demographic characteristic to create models - what customers shared were mostly incomplete and imbalance data; (2) Customer behavior is extracted from social media - extracting data from social media at present is not easy (3) Content-based, and collaborative-filtering uses customer ratings on products to recommend products - content-based engine loops into the same product zone, and not all e-commerce platforms have a function to rate products numerically; (4) Almost no one discussed time directionality on their recommendation engines. Our study uses customer purchase history which is always complete and has balanced data. This study considers the frequency of each product bought by customers on collaborative-filtering, and vectors representing customers are defined in a way to consider time directionality. Evaluations indicate our recommendation engine is better at recommending products with 69.93% accuracy level.
Recommendation Engine, Product Similarity, User Similarity, Taste Communities