Long-term Preferences Research Articles

Improving current interest with item and review sequential patterns for sequential recommendation

Sequential recommendation (SR) aims to recommend items based on user information and behavior sequences. Almost all the existing works for SR construct short-term preference and long-term preference only based on the user–item interactions or the reviews rather than considering the two types of information simultaneously. In fact, interaction items and reviews commonly reflect the user’s semantic information, and play significant roles in modeling the user preference. In this paper, we propose a novel model named Parallel Item sequential pattern and Review Sequential Pattern (PIRSP) for the sequential recommendation. Specifically, first, PIRSP learns two levels of sequential patterns from item and review information, respectively: (1) item sequential pattern, which uses a gated recurrent unit with an item-attention mechanism to model history behavior sequences; (2) review sequential pattern, which takes a convolution neural network with a target-attention mechanism for modeling associated reviews of interaction items. Then, we introduce a fusion gating mechanism for selectively combining the two sequential patterns to learn the short-term preference. Second, we employ a convolution neural network with aspect information to learn the long-term preference. Finally, we utilize a linear fusion on the long-term preference and short-term preference for modeling user preference and making final recommendation. The experimental results indicate that our model outperforms other state-of-the-art methods on the Amazon dataset. Our analysis of PIRSP’s recommendation process shows the positive effect of the two types of information and fusion gating mechanism on the performance of sequential recommendation.

Session-aware recommendation: A surprising quest for the state-of-the-art

Recommender systems are designed to help users in situations of information overload. In recent years we observed increased interest in session-based recommendation scenarios, where the problem is to make item suggestions to users based only on interactions observed in an ongoing session, e.g., on an e-commerce site. However, in cases where interactions from previous user sessions are also available, the recommendations can be personalized according to the users’ long-term preferences, a process called session-aware recommendation. Today, research in this area is scattered, and many works only compare a newly proposed session-aware with existing session-based models. This makes it challenging to understand what represents the state-of-the-art. To close this research gap, we benchmarked recent session-aware algorithms against each other and against a number of session-based recommendation algorithms along with heuristic extensions thereof. Our comparison, to some surprise, revealed that (i) simple techniques based on nearest neighbors consistently outperform recent neural techniques and that (ii) session-aware models were mostly not better than approaches that do not use long-term preference information. Our work therefore points to potential methodological issues where new methods are compared to weak baselines, and it also indicates that there remains a huge potential for more sophisticated session-aware recommendation algorithms.

Predictors of Employees’ Preference for Working from Home Post-Pandemic

The global pandemic of 2020-21 has enabled an examination of the conditions under which working from home is preferred. We examine whether work-life conflict (both work interfering with family and family interfering with work) and need fulfillment (autonomy, relatedness, competence) can be used to predict employees’ preference for working from home in the future, post-pandemic. With a sample of 944 employees working from home for the first time, this study found that work-life conflict was negatively related and need fulfillment was positively related to employees’ preference for working from home post-pandemic. The experience of having children at home or a partner who was also working from home did not affect employees’ long-term preference for working from home; however, being female did. Women were less likely to want to work from home post-pandemic. The implications for ways to maximize the experience of working from home in the future are discussed.

An End-to-End Review-Based Aspect-Level Neural Model for Sequential Recommendation

Users’ reviews of items contain a lot of semantic information about their preferences for items. This paper models users’ long-term and short-term preferences through aspect-level reviews using a sequential neural recommendation model. Specifically, the model is devised to encode users and items with the aspect-aware representations extracted globally and locally from the user-related and item-related reviews. Given a sequence of neighbor users of a user, we design a hierarchical attention model to capture union-level preferences on sequential patterns, a pointer model to capture individual-level preferences, and a traditional attention model to balance the effects of both union-level and individual-level preferences. Finally, the long-term and short-term preferences are combined into a representation of the user and item profiles. Extensive experiments demonstrate that the model substantially outperforms many other state-of-the-art baselines substantially.

HAM: Hybrid Associations Models for Sequential Recommendation.

Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models (HAM) to generate sequential recommendations. using three factors: 1) users' long-term preferences, 2) sequential, high-order and low-order association patterns in the users' most recent purchases/ratings, and 3) synergies among those items. HAM uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that HAM models significantly outperform the state of the art in all the experimental settings. with an improvement as much as 46.6%. In addition, our run-time performance comparison in testing demonstrates that HAM models are much more efficient than the state-of-the-art methods. and are able to achieve significant speedup as much as 139.7 folds.

Memory Augmented Hierarchical Attention Network for Next Point-of-Interest Recommendation

Next point-of-interest (POI) recommendation has been an important task for location-based intelligent services. However, the application of such promising technique is still limited due to the following three challenges: 1) the difficulty of capturing complicated spatiotemporal patterns of user movements; 2) the hardness of modeling fine-grained long-term preferences of users; and 3) the effective learning of interaction between long- and short-term preferences. Motivated by this, we propose a memory augmented hierarchical attention network (MAHAN), which considers both short-term check-in sequences and long-term memories. To capture the complicated interest tendencies of users within a short-term period, we design a spatiotemporal self-attention network (ST-SAN). For long-term preferences modeling, we employ a memory network to maintain fine-grained preferences of users and dynamically operate them based on users' constantly updated check-ins. Moreover, we first employ a coattention network/mechanism to integrate the proposed ST-SAN and memory network, which can fully learn the dynamic interaction between long- and short-term preferences. Our extensive experiments on two publicly available data sets demonstrate the effectiveness of MAHAN.

THEORETICAL AND APPLIED ASPECTS OF PUBLIC ADMINISTRATION IN THE FIELD OF NATIONAL SECURITY OF UKRAINE: RETROSPECTIVE ANALYSIS

The article is assigned to the researcheses of problems of public administration in the national security from the point of view of retrospective analysis. The emphasis is on respect for the fact that the state policy in the spheres of national security and defense is focused on the defense of the most important rights, legal interests and guarantees, in particular, man and citizen - their life and dignity, constitutional rights and freedoms, safe living conditions; society - its democratic values, prosperity and conditions for sustainable development; the state - its constitutional order, sovereignty, territorial integrity and inviolability; territory, environment - from emergencies. The outlined postulates occupy a prominent place in domestic legislation and become a fundamental basis for the implementation of various programs and concepts aimed at their practical implementation in public life. At the same time, in order to achieve the outlined aspects, the question of the theoretical foundations of public administration of national security in the context of retrospective analysis shall be important. Based on the results of doctrinal and comprehensive research, it is concluded that the theoretical and methodological difficulties of the retrospective concept of "national security" also allow us to conclude that it is expedient to consider not only the concept of "security" but also the concepts of "nation" and "nationalism" to find out what meaning is embedded in the concepts of "national interests" and "national values". Therefore, an important step that to some extent allows us to reach a consensus on the concept of "national security" is to consider the concept of "nation" and the phenomenon of the phenomenon called "nationalism". At the same time, the isolation of certain factors in the context of national security should be considered a temporary aspect, as it is only possible to assert the priority of certain national interests in these areas in a historical period of society and state, and hence the corresponding priority ensuring their implementation. Moreover, the long-term preference of one or another area under certain conditions may even be a threat to national security. Ignoring national interests in other areas ultimately leads first to a reduction in the potential of the state, and then to an integrated level of national security. The advantage that can be given to one or another sphere of national security is justified only under certain extraordinary conditions.

Wendepunkt Corona-Krise? Deutsche Präferenzen zur europäischen Integration staatlicher Kerngewalten seit Maastricht

Did the Covid-19 crisis prompt a turn in German EU policy? Investigating the long-term development of German preferences on the European integration of core state powers, we find striking continuity. German governments persistently seek to minimise their costs from and maximise their control over integration. Consequently, they back supranational capacity-building primarily as a last resort in existential systemic crises. The Corona recovery fund, which is temporary and placed under intergovernmental control, is in line with this general long-term preference and does little to alleviate the structural flaws of the Euro area.

Long- and short-term self-attention network for sequential recommendation

With great value in real applications, sequential recommendation aims to recommend users the personalized sequential actions. To achieve better performance, it is essential to consider both long-term preferences and sequential patterns (i.e., short-term dynamics). Compared to widely used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), Self-Attention Network (SAN) obtains a surge of interest due to fewer parameters, highly parallelizable computation, and flexibility in modeling dependencies. However, existing SAN-based models are inadequate in characterizing and distinguishing users’ long-term preferences and short-term demands since they do not emphasize the importance of the current interest and temporal order information of sequences. In this paper, we propose a novel multi-layer long- and short-term self-attention network (LSSA) for sequential recommendation. Specifically, we first split the entire sequence of a user into multiple sub-sequences according to the timespan. Then the first self-attention layer learns the user’s short-term dynamics based on the last sub-sequence, while the second one captures the user’s long-term preferences through the previous sub-sequences and the last one. Finally, we integrate the long- and short-term representations together to form the user’s final hybrid representation. We evaluate the proposed model on three real-world datasets, and our experimental results show that LSSA outperforms state-of-the-art methods with a wide margin.

Neural Attentive Travel package Recommendation via exploiting long-term and short-term behaviors

Travel package recommendation is a critical task in the tourism e-commerce recommender systems. Recently, an increasing number of studies proposed various travel package recommendation algorithms to improve Online Travel Agencies (OTAs) service, such as collaborative filtering-based, matrix factorization-based and neural network-based methods. Despite their value, however, the main challenges that incorporating complex descriptive information of the travel packages and capturing complicated users’ long-term preferences for fine-grained travel package recommendation are still not fully resolved. In terms of these issues, this paper propose a novel model named Neural Attentive Travel package Recommendation (NATR) for tourism e-commerce by combining users’ long-term preferences with short-term preferences. Specifically, NATR mainly contains two core modules, namely, travel package encoder and user encoder. The travel package encoder module is developed to learn a unified travel package representation by an attentive multi-view learning approach including word-level and view-level attention mechanisms. The user encoder module is designed to study long-term and short-term preference of the user by Bidirectional Long Short-Term Memory (Bi-LSTM) neural networks with package-level attention mechanism. In addition, we further adopt a gated fusion approach to coalesce these two kinds of preferences for learning high-quality the user’s representation. Extensive experiments are conducted on a real-life tourism e-commerce dataset, the results demonstrate the proposed model yields significant performance advantages over several competitive methods. Further analyses from different attention weights provide insights of attentive multi-view learning and gated fusion network, respectively.

Empirical analysis of session-based recommendation algorithms

Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with the user in an ongoing session and do not require the existence of long-term preference profiles. Most recently, a number of deep learning-based (“neural”) approaches to session-based recommendations have been proposed. However, previous research indicates that today’s complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy. With this work, our goal is to shed light on the state of the art in the area of session-based recommendation and on the progress that is made with neural approaches. For this purpose, we compare twelve algorithmic approaches, among them six recent neural methods, under identical conditions on various datasets. We find that the progress in terms of prediction accuracy that is achieved with neural methods is still limited. In most cases, our experiments show that simple heuristic methods based on nearest-neighbors schemes are preferable over conceptually and computationally more complex methods. Observations from a user study furthermore indicate that recommendations based on heuristic methods were also well accepted by the study participants. To support future progress and reproducibility in this area, we publicly share the session-rec evaluation framework that was used in our research.

A coarse-to-fine user preferences prediction method for point-of-interest recommendation

Point-of-interests (POIs) recommendations aim at recommending locations to users on social platforms by analyzing their histories or combining other information. At present, the different granularity of factors (i.e. time, geography and sociability) are not thoroughly studied in existing works. To deal with this problem, we propose a two-stage coarse-to-fine POI recommendation algorithm based on tensor factorization and weighted distance kernel density estimation (KDE). At first stage, we take account of not only long-term preferences with sequential context, but also the crowd’s preferences to estimate the coarse user-category interest. And then a specific-designed weighted KDE with consideration of spatial distance is employed to determine the fine-grained user-location interest. To evaluate the proposed method, experiments are conducted on two real benchmark location-based social network (LBSN) datasets. And the results show that the proposed method outperforms the state-of-the-art methods and produces better POI recommendation.

Screening and Surveillance Colonoscopy and COVID-19: Avoiding More Casualties

Screening and Surveillance Colonoscopy and COVID-19: Avoiding More Casualties

Attention with Long-Term Interval-Based Deep Sequential Learning for Recommendation

Modeling user behaviors as sequential learning provides key advantages in predicting future user actions, such as predicting the next product to purchase or the next song to listen to, for the purpose of personalized search and recommendation. Traditional methods for modeling sequential user behaviors usually depend on the premise of Markov processes, while recently recurrent neural networks (RNNs) have been adopted to leverage their power in modeling sequences. In this paper, we propose integrating attention mechanism into RNNs for better modeling sequential user behaviors. Specifically, we design a network featuring Attention with Long-term Interval-based Gated Recurrent Units (ALI-GRU) to model temporal sequences of user actions. Compared to previous works, our network can exploit the information of temporal dimension extracted by time interval-based GRU in addition to normal GRU to encoding user actions and has a specially designed matrix-form attention function to characterize both long-term preferences and short-term intents of users, while the attention-weighted features are finally decoded to predict the next user action. We have performed experiments on two well-known public datasets as well as a huge dataset built from real-world data of one of the largest online shopping websites. Experimental results show that the proposed ALI-GRU achieves significant improvement compared to state-of-the-art RNN-based methods. ALI-GRU is also adopted in a real-world application and results of the online A/B test further demonstrate its practical value.

Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

Session-aware news recommendations using random walks on time-evolving heterogeneous information networks

Traditional news media Web sites usually provide generic recommendations that are not personalized to the preferences of their users. Typically, news recommendation algorithms mainly rely on the long-term preferences of users and do not adjust their model to the continuous stream of short-lived incoming stories to capture short-term intentions revealed by users’ sessions. In this paper, we therefore study the problem of session-aware recommendations by running random walks on dynamic heterogeneous graphs. Concretely, we construct a heterogeneous information network consisting of users, news articles, news categories, locations and sessions. By using different (1) sliding time window sizes, (2) sub-graphs for model learning, (3) sequential article weighting strategies and (4) more diversified random walks, we perform recommendations in a second step. Our algorithm proposal is evaluated on three real-life data sets, and we demonstrate that our method outperforms state-of-the-art methods by delivering more accurate and diversified recommendations.

Learning evolving user’s behaviors on location-based social networks

With the popularity of smart phones, users’ activities on location-based social networks (LBSNs) evolve faster than traditional social networks. Existing models focus on modeling users’ long-term preferences, leveraging social collaborative filtering to enhance prediction performance. However, the dynamic mobility mechanism of user’s check-in behaviors on LBSNs is seldom considered. In this paper, we propose a new dynamic model that considers both geo-aware user preferences and the social interaction excitation arising from social connections to learn the dynamic mobility mechanism of user’s behaviors on LBSNs. Geo-aware location features, such as semantic features, latent features and dynamic features, are utilized to characterize the location information and reveal the evolution of the geographical impact of location. These geo-aware location features enable us to exploit user’s personal preferences. Meanwhile, we integrate a user’s social connections and friends’ preferences for modeling social interaction excitations. Finally, we jointly incorporate geo-aware user preference learning and social interaction excitation modeling to create a conditional intensity function for temporal point processes with which to explore the dynamic mobility mechanism of evolving user’s check-in behaviors on LBSNs. Extensive experiments on several real-world check-in datasets confirm that our proposed algorithm performs better than existing state-of-the-art methods.

Towards a Deep Attention-Based Sequential Recommender System

With the availability of a large amount of user-generated online data, discovering users’ sequential behaviour has become an integral part of a Sequential Recommender System (SRS). Combining the recent observed items (i.e., short-term preferences) with prior interacted items (i.e., long-term preferences) has gained increasing attention in recent years. However, the existing methods mostly assume that all the adjacent items in a sequence are highly dependent, which may not be practical in real-world scenarios due to the uncertainty of customers’ shopping behaviours. A user-item interaction sequence may contain some irrelevant items which may in turn lead to false dependencies between items. Moreover, current studies usually assign a static representation to each item when modeling a user’s long-term preferences. Therefore, they cannot differentiate the contributions of the items. Specifically, these two types of users’ preferences have been separately modeled and then linearly combined, which may fail to model complicated user-item interactions. In order to overcome the above mentioned problems, we propose a novel Deep Attention-based Sequential (DAS) model. DAS consists of three different blocks, $(i)$ an embedding block: which embeds users and items into low-dimensional spaces; $(ii)$ an attention block: which aims to discriminatively learn dependencies among items in both users’ long-term and short-term item sets; and $(iii)$ a fully-connected block : which first learns a mixture of users’ preferences representation through a nonlinear way and then combines it with users’ embeddings to have a personalized recommendation. Extensive experiments demonstrate the superiority of our proposed model compared to the state-of-the-art approaches in SRSs.

A Hybrid-Preference Neural Model for Basket-Sensitive Item Recommendation

Basket-Sensitive Item Recommendation (BSIR) is a challenging task that aims to recommend an item to add to the current basket given a user’s historical behaviors. The recommended item is supposed to be relevant to the items in current basket. Previous works mainly produce a recommendation based on user’s current basket, ignoring the inherent preference released by user’s long-term behaviors and failing to accurately distinguish the item importance in the basket for detecting user intent. To tackle the above issues, we propose a hybrid model, i.e., Hybrid-Preference Neural Model (HPNM), where a user’s inherent preference is recognized by modeling the historical sequential baskets and the recent preference is identified by focusing on the current basket. In detail, we apply an attention mechanism for distinguishing the importance of items in a basket to generate an accurate basket representation. GRU is utilized for modeling the basket-level sequential information to obtain user’s long-term preference and the representation of the current session is regarded as user’s short-term preference. We evaluate the performance of our proposals against the state-of-the-art baselines in the field of BSIR on two public datasets, i.e., TaFeng and Foursquare. The experimental results show that HPNM can achieve obvious improvements against the baselines in terms of HLU and Recall. In addition, we find HPNM with an attention mechanism can lead to a larger improvement against the baseline for item recommendation in terms of HLU and Recall on testing baskets with relatively fewer items.

A Neighbor-Guided Memory-Based Neural Network for Session-Aware Recommendation

Session-aware recommendation is a special form of session-based recommendation, where users' historical interactions before the current session are available. Among the existing session-aware recommendation studies, recurrent neural network (RNN) is a popular choice to model users' current intent of the ongoing session as well as their general preference implied in previous sessions. However, these RNN-based methods present limited memory so as to have difficulty in characterizing long-term behaviour. In addition, when modeling a user's preference, existing studies mainly refer to his (her) own sessions, while ignore the collaborative information from sessions of other users that may share similar tastes. To tackle the above problems, we propose a neighbor-guided memory-based neural network (MNN) for session-aware recommendation task, which comprehensively considers users' short-term intent, long-term preference and cross-sessions information to yield the final recommendations. Specifically, we design a long-term memory generator to capture users' general preference from their historical sessions, meanwhile leverage the neighbor sessions of current session to obtain cross-session collaborative information. Furthermore, with the guidance of long-term memory and cross-session information, we employ a short-term memory generator to yield users' ongoing intent, which serves as the dominating part of recommendation. Extensive experimental results on three real world datasets show the effectiveness of our model, with improvements up to 12.0% on 30MUSIC, 26.5% on NOWPLAYING and 13.5% on TMALL in terms of Recall@20, respectively. Analysis on current session length and the number of historical interacted items comprehensively demonstrates the superiority of our proposal on processing long sessions and modeling users' long-term behaviour.