Abstract: The present disclosure discloses a method and system based on contrastive learning for assisting a recommendation system to defend against shilling attacks, and belongs to the field of recommendation system security. The method includes: establishing an interaction matrix using interaction data; achieving data augmentation by using the target user's neighbor nodes as positive samples and a non-target user's neighbor nodes as negative samples; establishing a contrastive learning model, a user detection model, and a recommendation model resistant to shilling attacks, performing joint training on the three models, computing user-item interaction scores after training, and taking the top-20 scoring interacted items to form a recommendation list for the user. Based on a recommendation system, the present disclosure combines contrastive learning and a multi-layer perceptron to detect fake users and utilizes confidence values as weights to implement a recommendation system method resistant to shilling attacks.

Abstract: This application describes techniques for providing computer vision for manual services. In some instances, a remote system may determine that a current time is within a threshold period of time of a scheduled service and, based on the determination, send a first message requesting image data to a camera apparatus located within an environment. After sending the first message, the remote system may receive image data associated with the environment from the camera apparatus and use the image to detect an object within the environment. The remote system can then send a second message to a device of a guest and a third message to a device of a user, where each of the second message and the third message indicates that the object is within the environment. In some instances, the object can include an animal while in some instances, the object can include a person.

Abstract: Techniques are generally described for scene change detection. A first and second histogram representing frames of a video may be received. A Euclidean distance between the first and second histogram may be determined. A third histogram of a third frame may be received. Values of the third histogram may be compared to corresponding values of a background model of the environment. A fourth frame and a fifth frame of image data of the video may be received. A flow value between a first block of pixels of the fourth frame and a corresponding second block of pixels of the fifth frame may be determined. The flow value may be determined based on a motion vector and a sum of absolute differences between the first and second block. A portion of the video may be streamed to a remote computing device if the flow value exceeds a threshold value.

Abstract: This application describes techniques for providing computer vision for manual services. In some instances, a remote system may determine that a current time is within a threshold period of time of a scheduled service and, based on the determination, send a first message requesting image data to a camera apparatus located within an environment. After sending the first message, the remote system may receive image data associated with the environment from the camera apparatus and use the image to detect an object within the environment. The remote system can then send a second message to a device of a guest and a third message to a device of a user, where each of the second message and the third message indicates that the object is within the environment. In some instances, the object can include an animal while in some instances, the object can include a person.

Abstract: This application describes techniques for providing computer vision-based surveillance of in-home services. In some instances, a remote system may authenticate a guest at an environment using data that the remote system receives from a camera apparatus. To authenticate the guest, the remote system can determine that an identification of the guest matches an identification of a worker associated with a scheduled service at the environment. When the scheduled service includes an in-home delivery, the remote system can further determine that a package in possession of the guest matches an item for the scheduled in-home delivery. In some instances, after the guest enters the environment, the remote system can further monitor the environment to ensure that the guest stays within a designated location of the environment. The remote system can further send a message to an electronic device when the guest is outside of the designated location.