A Factored Relevance Model for Contextual Point-of-Interest Recommendation

The challenge of providing personalized and contextually appropriate recommendations to a user is faced in a range of use-cases, e.g., recommendations for movies, places to visit, articles to read etc. In this paper, we focus on one such application, namely that of suggesting 'points of interest' (POIs) to a user given her current location, by leveraging relevant information from her past preferences. An automated contextual recommendation algorithm is likely to work well if it can extract information from the preference history of a user (exploitation) and effectively combine it with information from the user's current context (exploration) to predict an item's 'usefulness' in the new context. To balance this trade-off between exploration and exploitation, we propose a generic unsupervised framework involving a factored relevance model (FRLM), comprising two distinct components, one corresponding to the historical information from past contexts, and the other pertaining to the information from the local context. Our experiments are conducted on the TREC contextual suggestion (TREC-CS) 2016 dataset. The results of our experiments demonstrate the effectiveness of our proposed approach in comparison to a number of standard IR and recommender-based baselines

Processing and Linking Audio Events in Large Multimedia Archives: The EU inEvent Project

In the inEvent EU project [1], we aim at structuring, retrieving, and sharing large archives of networked, and dynamically changing, multimedia recordings, mainly consisting of meetings, videoconferences, and lectures. More specifically, we are developing an integrated system that performs audiovisual processing of multimedia recordings, and labels them in terms of interconnected “hyper-events ” (a notion inspired from hyper-texts). Each hyper-event is composed of simpler facets, including audio-video recordings and metadata, which are then easier to search, retrieve and share. In the present paper, we mainly cover the audio processing aspects of the system, including speech recognition, speaker diarization and linking (across recordings), the use of these features for hyper-event indexing and recommendation, and the search portal. We present initial results for feature extraction from lecture recordings using the TED talks. Index Terms: Networked multimedia events; audio processing: speech recognition; speaker diarization and linking; multimedia indexing and searching; hyper-events. 1

The use of intellectual capital information by sell-side analysts in company valuation

This paper investigates the role of intellectual capital information (ICI) in sell-side analysts’ fundamental analysis and valuation of companies. Using in-depth semi-structured interviews, it penetrates the black box of analysts’ valuation decision-making by identifying and conceptualising the mechanisms and rationales by which ICI is integrated within their valuation decision processes. We find that capital market participants are not ambivalent to ICI, and ICI is used: (1) to form analysts’ perceptions of the overall quality, strengths and future prospects of companies; (2) in deriving valuation model inputs; (3) in setting price targets and making investment recommendations; and (4) as an important and integral element in analyst–client communications. We show that: there is a ‘pecking order’ of mechanisms for incorporating ICI in valuations, based on quantifiability; IC valuation is grounded in valuation theory; there are designated entry points in the valuation process for ICI; and a number of factors affect analysts’ ICI use in valuation. We also identify a need to redefine ‘value-relevant’ ICI to include non-price-sensitive information; acknowledge the boundedness and contextuality of analysts’ rationality and motives of their ICI use; and the important role of analyst–client meetings for ICI communication

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

Sell-side analysts’ valuation method choices and the role of ESG information in renewable energy valuations - case Neste Oyj

The objective of this research was to gain understanding of the drivers behind sell-side analysts’ valuation method choices for valuing a particular company, which have largely remained unknown. The phenomenon was explored through a renewable energy case company Neste Oyj. Additionally, due to the context of renewable energy valuations, the study sought to investigate how environmental, social and governance information influence valuations. The main research question of the study was: What affects analysts’ valuation method choices directly linked to a firm's target price? The secondary research question, to address the context of renewable energy valuations, was: What is the role of environmental, social and governance information in the context of renewable energy valuation? This research followed a qualitative research approach. Empirical data was gathered from 6 in-depth semi-structured interviews of sell-side analysts covering the case company Neste Oyj. The interviews were based on the content analysis of the interviewed analysts’ latest valuation reports on Neste Oyj. Further, the interviews were constructed acknowledging prior research. The interviewees were based in the UK and the Nordics. Interviews were conducted either face-to-face or by phone. Thus, complimentary research material obtained from interviewees’ was considered. Further, the research approach was abductive. The findings of the research supported the acknowledgement that the decision-environment that analysts face is multidimensional, having various drivers affecting choices made. First, the findings corroborated prior empirical evidence on factors influencing valuation method choices, such as client preferences. Secondly, the research found indications of theoretically suggested factors and thirdly, identified new factors. The factors influencing valuation method choices were categorized under four groups of valuation method drivers, constructing a framework for assessing the phenomenon: 1) employer related, 2) market deriving, 3) method characteristics and personal preferences and 4) firm specific drivers. Additionally, the research noted analysts’ changing valuation method preferences: the shift from the dominance of PE to preference of enterprise value multiples. Secondarily, the research found ESG information to play a secondary role in the context of renewable energy valuations and noted UK and Nordic analysts differing perceptions on the valuation relevance of ESG-information. ESG did not influence valuation method choices or target prices explicitly. However, for Nordic analysts, ESG-information could influence the stock recommendation and be present in screenings or reports