Local Popularity and Time in top-N Recommendation

Items popularity is a strong signal in recommendation algorithms. It strongly affects collaborative filtering approaches and it has been proven to be a very good baseline in terms of results accuracy. Even though we miss an actual personalization, global popularity can be effectively used to recommend items to users. In this paper we introduce the idea of a time-aware personalized popularity in recommender systems by considering both items popularity among neighbors and how it changes over time. An experimental evaluation shows a highly competitive behavior of the proposed approach, compared to state of the art model-based collaborative approaches, in terms of results accuracy.Comment: ECIR short paper, 7 page

Designing and evaluating recommender systems with the user in the loop

On many of today's most popular Internet service platforms, users are confronted with a seemingly endless number of options to choose from, such as articles to purchase on online shopping sites, music to listen to on online streaming platforms, or posts to read on social media. As a solution to this choice overload problem, recommender systems have been integrated into more and more websites and applications to help users find items that they might like or that could be useful in their current choice situation. In recent decades, research on recommender systems has mostly been driven by offline performance comparisons, in which each new approach is compared to the state of the art in terms of its ability to retroactively predict user preferences in historical data sets. However, such a purely algorithmic research approach can only capture one of the many factors that contribute to a useful and engaging recommendation experience from a user perspective. In fact, a variety of aspects can influence how recommendations affect users' decision-making processes and how users perceive recommendations, including details regarding the recommender system's user interface or subconscious cognitive effects evoked by the recommendations. In this thesis by publication, selected works of the author are presented that investigate different aspects pertaining to the design and evaluation of recommender systems from a more user-focused perspective. The first part of the thesis outlines each of these publications and positions them within the research context. The presented works investigate (i) how recommender systems interact with their users, (ii) how recommender systems should be evaluated with the user in mind, (iii) possible biases in user studies, (iv) an algorithmic strategy to re-rank recommendation lists according to individual user tendencies, and (v) two phenomena based on which recommendations can subconsciously influence user decision-making processes. The second part of the thesis, the appendix, contains the aforementioned publications in full. The presented studies demonstrate that it is imperative to design and evaluate recommender systems with the user in mind, taking into account the intricacies of interaction details, recommendation list composition, user context, and decision-making processes

Item Familiarity Effects in User-Centric Evaluations of Recommender Systems

ABSTRACT Laboratory studies are a common way of comparing recommendation approaches with respect to different quality dimensions that might be relevant for real users. One typical experimental setup is to first present the participants with recommendation lists that were created with different algorithms and then ask the participants to assess these recommendations individually or to compare two item lists. The cognitive effort required by the participants for the evaluation of item recommendations in such settings depends on whether or not they already know the (features of the) recommended items. Furthermore, lists containing popular and broadly known items are correspondingly easier to evaluate. In this paper we report the results of a user study in which participants recruited on a crowdsourcing platform assessed system-provided recommendations in a between-subjects experimental design. The results surprisingly showed that users found non-personalized recommendations of popular items the best match for their preferences. An analysis revealed a measurable correlation between item familiarity and user acceptance. Overall, the observations indicate that item familiarity can be a potential confounding factor in such studies and should be considered in experimental designs

Item Familiarity Effects in User-Centric Evaluations of Recommender Systems

ABSTRACT Laboratory studies are a common way of comparing recommendation approaches with respect to different quality dimensions that might be relevant for real users. One typical experimental setup is to first present the participants with recommendation lists that were created with different algorithms and then ask the participants to assess these recommendations individually or to compare two item lists. The cognitive effort required by the participants for the evaluation of item recommendations in such settings depends on whether or not they already know the (features of the) recommended items. Furthermore, lists containing popular and broadly known items are correspondingly easier to evaluate. In this paper we report the results of a user study in which participants recruited on a crowdsourcing platform assessed system-provided recommendations in a between-subjects experimental design. The results surprisingly showed that users found non-personalized recommendations of popular items the best match for their preferences. An analysis revealed a measurable correlation between item familiarity and user acceptance. Overall, the observations indicate that item familiarity can be a potential confounding factor in such studies and should be considered in experimental designs

Tunable coupling by means of oxygen intercalation and removal at the strongly interacting graphene/cobalt interface

It is well known that intercalated species can strongly affect the graphene-substrate interaction. As repeatedly shown by experiment and theory, the intercalation of atomic species may establish a free-standing character in chemisorbed graphene systems. Here, we focus on graphene grown on a strongly interacting support, cobalt, and demonstrate that the film electronic structure and doping can be tuned via the intercalation/removal of interfacial oxygen. Importantly, cathode lens microscopy reveals the main mechanism of oxygen intercalation, and in particular how microscopic openings in the mesh enable oxygen accumulation at the graphene-cobalt interface. Our experiments show that this process can be carefully controlled through temperature, without affecting the film morphology and crystalline quality. The presence of oxygen at the interface induces an upward shift of the graphene π band, moving its crossing above the Fermi level, accompanied by an increased Fermi velocity and reduced momentum width. Control on the graphene coupling to cobalt may enable one to alter the induced spin polarization in graphene’s electronic states

Semiconductor Halogenation in Molecular Highly‐Oriented Layered p–n (n–p) Junctions

Organic p–n junctions attract widespread interest in the field of molecular electronics because of their unique optoelectronic singularities. Importantly, the molecular donor/acceptor character is strongly correlated to the degree of substitution, e.g., the introduction of electron-withdrawing groups. Herein, by gradually increasing the degree of peripheral fluorination on planar, D4h−symmetric iron(II) phthalocyanato (FePc) complexes, the energy level alignment and molecular order is defined in a metal-supported bilayered Pc-based junction using photoemission orbital tomography. This non-destructive method selectively allows identifying molecular levels of the hetero-architectures. It demonstrates that, while the symmetric fluorination of FePc does not disrupt the long-range order and degree of metal-to-molecule charge transfer in the first molecular layer, it strongly impacts the energy alignment in both the interface and topmost layer in the bilayered structures. The p–n junction formed in the bilayer of perhydrogenated FePc and perfluorinated FeF16Pc may serve as an ideal model for understanding the basic charge-transport phenomena at the metal-supported organic–organic interfaces, with possible application in photovoltaic devices

Momentum-selective orbital hybridisation

When a molecule interacts chemically with a metal surface, the orbitals of the molecule hybridise with metal states to form the new eigenstates of the coupled system. Spatial overlap and energy matching are determining parameters of the hybridisation. However, since every molecular orbital does not only have a characteristic spatial shape, but also a specific momentum distribution, one may additionally expect a momentum matching condition; after all, each hybridising wave function of the metal has a defined wave vector, too. Here, we report photoemission orbital tomography measurements of hybrid orbitals that emerge from molecular orbitals at a molecule-on-metal interface. We find that in the hybrid orbitals only those partial waves of the original orbital survive which match the metal band structure. Moreover, we find that the conversion of the metal’s surface state into a hybrid interface state is also governed by momentum matching constraints. Our experiments demonstrate the possibility to measure hybridisation momentum-selectively, thereby enabling deep insights into the complicated interplay of bulk states, surface states, and molecular orbitals in the formation of the electronic interface structure at molecule-on-metal hybrid interfaces