Intent-Aware Contextual Recommendation System

Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve. Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness (or scoring) is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations. We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining (ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field cannot be longer than 1,920 characters," the abstract appearing here is slightly shorter than the one in the PDF fil

The development of an inclusive model to construct teacher’s professional knowledge: pedagogic content knowledge for sound-based music as a new subject area

The file attached to this record is the author's final peer reviewed version.This paper outlines a systematic process for developing the different knowledge domains required for teaching sound-based (electroacoustic) music as a new subject area. As a new area within the discipline of music, teachers are novices to the field. This requires epistemological deconstruction of what knowledge teachers need in this new field. Then the analysis outlines how to develop teachers’ new knowledge; which can be constructed as: subject content knowledge (SCK), pedagogic content knowledge (PCK) and technology pedagogic content knowledge (TPACK). This epistemological analysis informed our creation of teaching materials that develop these different knowledge domains and take account of the complex interplay between them. This process was demonstrated through the ElectroAcoustic Resource Site Projects to: build first subject content knowledge; then create teacher’s packs to build pedagogic content knowledge; and a bespoke CPD programme to embed their inter-relationships and build technology pedagogic content knowledge. Most importantly, creating the teacher’s packs employed a user-centred design approach, putting teachers and pupils in the centre of the development process, thereby giving them voice. Voice is an integral part of empowerment in our model, which is conceptualised as practicing ‘communicative action’ (Habermas 1984) and disrupts the hegemonic grip of the academic curriculum dominated by the tradition music canon. This paper adds to the knowledge-base regarding how to develop the different domains required for teaching a new subject. We argue that sound-based music is accessible to all teachers and learners, thereby increasing inclusivity. This in turn can radically disrupt ways of teaching music in schools and the model created provides the necessary scaffolding for a paradigm shift in music teaching on an international level

Children searching information on the Internet: Performance on children's interfaces compared to Google

Children frequently make use of the Internet to search for information. However, research shows that children experience many problems with searching and browsing the web. The last decade numerous search environments have been developed, especially for children. Do these search interfaces support children in effective information-seeking? And do these interfaces add value to today’s popular search engines, such as Google? In this explorative study, we compared children’s search performance on four interfaces designed for children, with their performance on Google. We found that the children did not perform better on these interfaces than on Google. This study also uncovered several problems that children experienced with these search interfaces, which can be of use for designers of future search interfaces for children

Design and Evaluation of a Probabilistic Music Projection Interface

We describe the design and evaluation of a probabilistic interface for music exploration and casual playlist generation. Predicted subjective features, such as mood and genre, inferred from low-level audio features create a 34- dimensional feature space. We use a nonlinear dimensionality reduction algorithm to create 2D music maps of tracks, and augment these with visualisations of probabilistic mappings of selected features and their uncertainty. We evaluated the system in a longitudinal trial in users’ homes over several weeks. Users said they had fun with the interface and liked the casual nature of the playlist generation. Users preferred to generate playlists from a local neighbourhood of the map, rather than from a trajectory, using neighbourhood selection more than three times more often than path selection. Probabilistic highlighting of subjective features led to more focused exploration in mouse activity logs, and 6 of 8 users said they preferred the probabilistic highlighting mode